Some thoughts - something of a rant, but interesting:
Engineering is built around figuring out how to do things, around solving problems. It's about making progress by discovering as many wrong solutions as you need before you find the right way. You try something, and then you find out if it works. It may take minutes or hours or days or weeks to see the results, or even seconds, but that delay is what separates engineering from art.
In art, you experiment and discover not to find one solution, not to document your mistakes, but to build your own internal, intuitive knowledge, to become fluent in the space of a system. Then you have the potential to create. It's about bridging the gap between intention - directed from within the self, not by a person or institution - and manifestation. The painter studies and practices and learns the characteristics of paints and brushes and surfaces, not in order to compile a manual or to solve a problem, but so that when he wants to paint something, he'll be able to paint it.
It's the difference between a slide show and an animation. It allows for that spark to appear between the frames. Once you are there, once you are fluent and exploring a space, with all the technicalities subsumed by your mind so you are free to create rather than to simply solve, then you are in the realm of design and artistic expression.
Oh, and if Daniel Pink is right, then engineering is also what may become automated or outsourced. I'm sticking with art. :p
Showing posts with label knowledge. Show all posts
Showing posts with label knowledge. Show all posts
2007/07/19
2005/11/30
Distributed Knowledge Networks
What is a "distributed knowledge network?" It is a network, of knowledge, that is distributed. A network is a collection of nodes that are linked together. Knowledge consists of the mechanisms that allow a system to interact with its environment. It is the embodiment of the knowledge of relationships between various patterns, which is extracted from the information coming into the system. A knowledge network is not the same as a learning machine, it is what is constructed by a learning machine. However, the knowledge network can be used by the learning machine to construct more knowledge, of course, since knowledge usually builds on previous knowledge. I think of a knowledge network as an artifact rather than a process, the product of knowledge production. Distributed knowledge networks are those built up by the actions of many separate agents, as well as stored in a diffuse set of agents.
2005/11/16
Analysis of Electric Sheep
The final chapter of Laboratory Life by Latour and Woolgar, presents a framework for understanding the construction of scientific facts. I will use this framework to analyze the knowledge production in Electric Sheep. This knowledge concerns the location of aesthetically pleasing fractal flames in the space of all possible fractal flames. Each fractal flame is an image produced from a seed of 84 real numbers. Essentially, each one may be defined as a point in an 84-dimensional space.
Using the framework in Laboratory Life, I can describe the Electric Sheep project as moving from chance to necessity. In the beginning, there is no way for you to know where the interesting fractal flames may be hiding, so all locations are equally probable. If you were to choose one to look at, you would have to make a random guess, only informed by chance. But as you build up knowledge of where the nice fractal flames may live, certain places are more probable than others. Eventually certain positions in this space become necessary to look at, as the probability of it containing interesting fractal flames becomes very high. This movement, from equal probability and chance to unequal probability and necessity, is the essence of knowledge production.
Latour and Woolgar's book describes six elements which work together to create this movement. First off there is construction, which is the slow, evolutionary process of accumulating facts. These facts are created through an agonistic process where hypotheses and their scientists compete to show that they are the best one. Once these facts are accepted they are embodied in laboratory equipment or skills and then taken for granted - materialization. This influences the fact's credibility, which refers to the cost in rejecting an established fact because of the time and energy invested in it. It is also important to recognize the role of individual circumstances in the production of knowledge, as knowledge is not so much about finding the universal truth as it is an idiosyncratic approximation to it. Finally, noise is a description of the initial state where all possibilities are equally probable, which facts try to limit into necessities.
In Electric Sheep, construction is the creation of a family tree of the best fractal flame images, or 'sheep.' Each one represents the knowledge that interesting sheep are more likely to be found nearby in 84-dimensional fractal flame space. Sheep are voted on for this privilege, which is agonistic in that it is competitive. Materialization takes place when the popular ones are saved as part of the family tree, to be used as the basis for later variations. Participants can not communicate to convince each other to vote for one sheep or another, so credibility does not play a noticeable role. People may submit their own fractal flames as stock for new sheep, and the direction the family tree branches from it is highly dependent on individual circumstances. It is extremely unlikely to chance upon the same fractal flame more than once, and the space of possibilities is so large that any knowledge network can only map a small portion of it. The end result is that in one corner of this fractal flame possibility space, the noise has been decreased so that some spots may be known to hold pretty fractal flames.
Using the framework in Laboratory Life, I can describe the Electric Sheep project as moving from chance to necessity. In the beginning, there is no way for you to know where the interesting fractal flames may be hiding, so all locations are equally probable. If you were to choose one to look at, you would have to make a random guess, only informed by chance. But as you build up knowledge of where the nice fractal flames may live, certain places are more probable than others. Eventually certain positions in this space become necessary to look at, as the probability of it containing interesting fractal flames becomes very high. This movement, from equal probability and chance to unequal probability and necessity, is the essence of knowledge production.
Latour and Woolgar's book describes six elements which work together to create this movement. First off there is construction, which is the slow, evolutionary process of accumulating facts. These facts are created through an agonistic process where hypotheses and their scientists compete to show that they are the best one. Once these facts are accepted they are embodied in laboratory equipment or skills and then taken for granted - materialization. This influences the fact's credibility, which refers to the cost in rejecting an established fact because of the time and energy invested in it. It is also important to recognize the role of individual circumstances in the production of knowledge, as knowledge is not so much about finding the universal truth as it is an idiosyncratic approximation to it. Finally, noise is a description of the initial state where all possibilities are equally probable, which facts try to limit into necessities.
In Electric Sheep, construction is the creation of a family tree of the best fractal flame images, or 'sheep.' Each one represents the knowledge that interesting sheep are more likely to be found nearby in 84-dimensional fractal flame space. Sheep are voted on for this privilege, which is agonistic in that it is competitive. Materialization takes place when the popular ones are saved as part of the family tree, to be used as the basis for later variations. Participants can not communicate to convince each other to vote for one sheep or another, so credibility does not play a noticeable role. People may submit their own fractal flames as stock for new sheep, and the direction the family tree branches from it is highly dependent on individual circumstances. It is extremely unlikely to chance upon the same fractal flame more than once, and the space of possibilities is so large that any knowledge network can only map a small portion of it. The end result is that in one corner of this fractal flame possibility space, the noise has been decreased so that some spots may be known to hold pretty fractal flames.
2005/11/08
A Distributed Knowledge Network
Electric Sheep
Electric Sheep is a distributed computing project which allows users to view fractal flames and vote for ones they like. To use it, you download a screensaver that will help generate the fractal flames and display them on the screen. Fractal flames are computer-generated animations which take a lot of processing power to produce, so each computer running the Electric Sheep screensaver does a little bit. A new fractal flame, or 'sheep,' is displayed on each participating computer about every ten minutes. While that sheep is on the screen, pressing the up arrow key will give it a vote. Only a few sheep are stored on the server at one time, so the sheep with more votes get to live longer. New sheep are variations on sheep still living on the server, so a sheep that got a lot of votes would have a lot of children that are similar to it. This means that Electric Sheep can explore the space of possible fractal flames to find pretty ones.
This project produces some very simple knowedge - collective knowledge of which fractal flames are interesting. Each saved sheep is a label that marks all similar sheep as potentially interesting forms. Electric Sheep takes the information of user's votes and embodies it as knowledge by using popular sheep as a basis for the next samples. I consider knowledge to be the structure of a system which is influenced by incoming information and then produces some action from it. In the case of Electric Sheep, its structure consists of the currently saved sheep along with the method by which it explores new fractal flames. The system uses this knowledge to make pretty pictures and to further develop its knowledge.
Electric Sheep is a distributed computing project which allows users to view fractal flames and vote for ones they like. To use it, you download a screensaver that will help generate the fractal flames and display them on the screen. Fractal flames are computer-generated animations which take a lot of processing power to produce, so each computer running the Electric Sheep screensaver does a little bit. A new fractal flame, or 'sheep,' is displayed on each participating computer about every ten minutes. While that sheep is on the screen, pressing the up arrow key will give it a vote. Only a few sheep are stored on the server at one time, so the sheep with more votes get to live longer. New sheep are variations on sheep still living on the server, so a sheep that got a lot of votes would have a lot of children that are similar to it. This means that Electric Sheep can explore the space of possible fractal flames to find pretty ones.
This project produces some very simple knowedge - collective knowledge of which fractal flames are interesting. Each saved sheep is a label that marks all similar sheep as potentially interesting forms. Electric Sheep takes the information of user's votes and embodies it as knowledge by using popular sheep as a basis for the next samples. I consider knowledge to be the structure of a system which is influenced by incoming information and then produces some action from it. In the case of Electric Sheep, its structure consists of the currently saved sheep along with the method by which it explores new fractal flames. The system uses this knowledge to make pretty pictures and to further develop its knowledge.
2005/10/18
Reaction to Online Credibility paper
The main idea of this paper is that the old ways of determining credibility, by checking sources, is becoming obsolete with the rise of the Internet and particularly collaborative sites such as Wikipedia. Instead, users find various cues on the site which give an overall impression of credibility. For example, on Wikipedia there is recent information, many images to complement articles, and external links, all of which make the site seem more credible. The perceived credibility of a site like Wikipedia also depends on the users' view of the process by which its content is created. A user who does not believe that a collaborative encyclopedia is feasible is not likely to give much credibility to the information in Wikipedia.
I think that source credibility actually can become an important factor though. A distinguished website or the author of a popular blog can hold credibility on their own. People may think an article on Wikipedia is credible simply because it is on Wikipedia. Basing the credibility of some information on its source is a way to avoid the work of analyzing it. I think it makes as much sense to attach source credibility to a website like Wikipedia as to a person. Both are, in a sense, distributed knowledge networks. One is made of webpages, while the other is made of neurons. Actually the distinction is fuzzier than that, as people rely on the external memory of papers and websites, while human contributors are integral to the knowledge network of Wikipedia.
I think that source credibility actually can become an important factor though. A distinguished website or the author of a popular blog can hold credibility on their own. People may think an article on Wikipedia is credible simply because it is on Wikipedia. Basing the credibility of some information on its source is a way to avoid the work of analyzing it. I think it makes as much sense to attach source credibility to a website like Wikipedia as to a person. Both are, in a sense, distributed knowledge networks. One is made of webpages, while the other is made of neurons. Actually the distinction is fuzzier than that, as people rely on the external memory of papers and websites, while human contributors are integral to the knowledge network of Wikipedia.
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