NetDynam 2.0

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Web 3.0 from Kate Ray on Vimeo.

This video from Kate Ray quickly made the rounds.

What a long way the net has come. I suppose it necessary but gratuitous to add: ‘for better and for worse.’

There’s a moment in this interesting mash-up where the speaker implies the following: could we re-render human brain to think more like a machine? This follows from the difficulty of making a machine think like a human.

I had to look up the use of the term ontologies because I know little about information science, and, the its use in the video seemed to depart from the philosophical term. Here’s the treatment about ontologies at wikipedia.

There is nothing about the problems faced by the varieties of user. I’m a user and I know of the problems I encounter in searching for information, both on the internet, in libraries, and, on my own computer, in my own archive of documents.

I’ll mention three challenges. I’ll frame this by stating that I wish my computer-based archives and library archives were indexed by google.

(1) usually, (my) searches for information on google are satisfied. However, because the results are matched with the real-time indexing my cognition provides for, the end of a search on a given topic–usually in the social sciences–is arbitrarily terminated. In other words, I have conclusive idea that a given result is the optimum result. I’d also characterize my search methods using partly ad hoc heuristics.

(2) searches in my computer-based archive are brute force and leverage Spotlite’s ability to look into the text of every file, BUT, involve scanning through very long result lists, most of which are not positive. As a user, the labor intensive task of organizing files on my end is, ‘too much.’ And, fit to this is the ease with which information can be archived versus the labor involved in organizing it. Somewhat: the intuitive’s curse…

(3) The most difficult search of the web and internet resources are those that are very particular and very local. A good example would be somebody’s address. Searches oriented to topics do not fall into this category.

One other note–I would guess my own search capability falls into the highly capable slice of any Bell Curve. This guess is based in my understanding of how to use the specific editing features of google search. And, it’s based on observing how most other people use search. One of the challenges for the semantic web, given,

The Semantic Web is an evolving development of the World Wide Web in which the meaning (semantics) of information on the web is defined, making it possible for machines to process it.

is any useful, more powerful interface and facilitation, has to meet the different modes of differentiated users.

For example, I wouldn’t be skeptical of a machine’s ability to qualify results so that I could be confident I’ve reached the optimum set of results, but I’d like to know beforehand why I needn’t be skeptical. And, this would have to be presented to me at my level.

Dreamlines, a web art generator by Leonardo Solaas.

Leonardo Solaas is a programmer. His focus point is on using Java as a platform, the web browser as an interface, and, data processing routines as, in effect, painter’s brushes. However my weak attempt at description defers to the artist’s own words,

“The thing is, now I spend most of my day in front of my loyal laptop, working as freelance developer & interface designer for the most interesting clients I manage to find, and going about my own experiments and ideas when I can get to that.

This site intends to be a hub for several kinds of traces left behind by my so-called ‘artistic’ practice, plus related pursuits. I’m not sure what all this ‘new media art’ thing is all about, but for me is a convenient playground where I can mash up all sorts of interests with relative freedom.”

This excerpt, from his short first person bio is tagged accordingly:

autnomous agents-blog-castellano-data visualization-design-digital image-drupal-experiment-flash-generative-hand -made-internet-me on myself-multiplicity-particle system-physical-processing-social-teaching-text-theory-workshop>

(Inspires me to think about what tags I’d apply to me.) Anyway…these tags cover a lot of ground.

Being fascinated with how computing power and user interaction can be used to create stuff, I fell right into Leonardo’s Dreamlines.

Like it is with other generators, the role I play is that of an Initiator. And, as it also is with the best of those generators, the Initiator also has to be a ‘chef of time;’ (inasmuch as I’ve learned to be patient and wait for resonant results.) What initiator/time chef waits for are rewarding moments in the stream of serendipitous visual mixing. The process is for me akin to music-making, yet the process isn’t anywhere as demanding.

I’ve noted over at Explorations blog,

Mechanical Kitsch, or New Frontier? further brief reflections about several of the issues raised by the ‘generator medium.’

Here’s several captures from mixes I initiated.

Title: Semiotics

Title: Found It

Then, it occurred to me I could try an experiment. My hypothesis was simple: if I captured the visual mix as it unfolded, how well might it coincide with some of my music? The main thing though was that I wasn’t going do anything but slap the two pieces together, so the experiment was seeking to hit rather than miss. This is different than editing music to expressly fit the visual.

I’ve posted the result over at noguts noglory studios. 21 minutes of abstract flow. (You can always turn down the audio!)

Quark

When I transferred the result using iMovie to a DVD and played it on the big HD screen, I was amazed at how good it looked.

There’s a sort of “future creativity” lurking in the seams of generativity, person-code, shallow manipulation, and, the immensity of the raw data archive.

The Stages of the Usage Lifecycle
The stages of the lifecycle are straightforward and simple. You can dive into lots more depth as your application warrants, and you can add stages, but for the most part these five stages apply to almost all software.
* Unaware This isn’t so much a stage as it is a starting point. Most people are in this stage: completely unaware of your product.
* Interested These people are interested in your product, but are not yet users. They have lots of questions about how it works and what value it provides.
* First-time Use These people are using your software for the first time, a crucial moment in their progression.
* Regular Use These people are those who use your software regularly and perhaps pay for the privilege.
* Passionate Use These people are the ultimate goal: passionate users who spread their passion and build a community around your software

Designing for the Social Web

(Also a test of remote posting tool.)

For my own purposes, I am known to speak of affectual topologies and affectual ecologies. (This having to do with memesis and anthropology–whatever.)

Mining the Web for Feelings, Not Facts
New York Times
By ALEX WRIGHT
Published: August 23, 2009

(excerpts)

1.
Computers may be good at crunching numbers, but can they crunch feelings?

The rise of blogs and social networks has fueled a bull market in personal opinion: reviews, ratings, recommendations and other forms of online expression. For computer scientists, this fast-growing mountain of data is opening a tantalizing window onto the collective consciousness of Internet users.

An emerging field known as sentiment analysis is taking shape around one of the computer world’s unexplored frontiers: translating the vagaries of human emotion into hard data.
This is more than just an interesting programming exercise. For many businesses, online opinion has turned into a kind of virtual currency that can make or break a product in the marketplace.

2.
Jodange, based in Yonkers, offers a service geared toward online publishers that lets them incorporate opinion data drawn from over 450,000 sources, including mainstream news sources, blogs and Twitter.

4.
Such tools could help companies pinpoint the effect of specific issues on customer perceptions, helping them respond with appropriate marketing and public relations strategies.

5.
While the more advanced algorithms used by Scout Labs, Jodange and Newssift employ advanced analytics to avoid such pitfalls, none of these services works perfectly. “Our algorithm is about 70 to 80 percent accurate,” said Ms. Francis, who added that its users can reclassify inaccurate results so the system learns from its mistakes.

Translating the slippery stuff of human language into binary values will always be an imperfect science, however. “Sentiments are very different from conventional facts,” said Seth Grimes, the founder of the suburban Maryland consulting firm Alta Plana, who points to the many cultural factors and linguistic nuances that make it difficult to turn a string of written text into a simple pro or con sentiment. “ ‘Sinful’ is a good thing when applied to chocolate cake,” he said.

The simplest algorithms work by scanning keywords to categorize a statement as positive or negative, based on a simple binary analysis (“love” is good, “hate” is bad). But that approach fails to capture the subtleties that bring human language to life: irony, sarcasm, slang and other idiomatic expressions. Reliable sentiment analysis requires parsing many linguistic shades of gray.

“We are dealing with sentiment that can be expressed in subtle ways,” said Bo Pang, a researcher at Yahoo who co-wrote “Opinion Mining and Sentiment Analysis,” one of the first academic books on sentiment analysis.

To get at the true intent of a statement, Ms. Pang developed software that looks at several different filters, including polarity (is the statement positive or negative?), intensity (what is the degree of emotion being expressed?) and subjectivity (how partial or impartial is the source?).

For example, a preponderance of adjectives often signals a high degree of subjectivity, while noun- and verb-heavy statements tend toward a more neutral point of view.

As sentiment analysis algorithms grow more sophisticated, they should begin to yield more accurate results that may eventually point the way to more sophisticated filtering mechanisms. They could become a part of everyday Web use.

Code-swarm, anyone?

This post starts a series aimed to point to a conception of Web 3.0 drawn from the deployment of the so-called semantic web for the purpose of having so-called machines read and interpret the data.

Amongst the inner circle here, it goes without saying that this has already been raised as a concept and direction, and it has been supposed this require text/lexical analytical tools.

For my own part, I assume lots of people and teams are working to build robust analytic tools. Also, it is most interesting to me personally to consider what are the ramifications of Web 3.0 for users who don’t give a whit about what is happening inside these machines; nor care much about the purposes implicit in the human direction prior to (and thus ‘behind,’) machine activities; nor are aware of the long history of efforts to realize effective and efficient data-mining/analysis tools for all sorts of commercial, security, law enforcement, research, purposes.


via Readwriteweb The Web of Data: Creating Machine-Accessible Information

via Twine: The Web of Identities: Making Machine-Accessible People Data

footnote found here: Cybernetick Inkwell

So what, then, are all the technologies like mashups, XML, Java and the rest, if not 2.0? I actually see them as web 3.0 technologies–not for the casual user or faint of heart. 1.0 was the early web, with its need for knowledge of code and servers; 2.0 is easy entry, democratization, and increased participation; 3.0 is about more complex connections being made.

via Social Computing Journal: Web 3.0: The Web Goes Industrial

Web 2.0 is social: many hands make light work. In stark contrast, Web 3.0 is industrial: the automation of tasks displaces human work. But trite definitions won’t prepare us for change. Whatever you call it, our information economy is in the midst of an Industrial Revolution. And if you don’t place the Web within the frame of industrial manufacturing, you won’t see the real disruptive change coming.

This story reads much like the first Industrial Revolution. Artisans and skilled tradesman used to create everything by hand. Then, through the emergence of a handful of technical innovations, came the age of mass production. It was a profound turning point in human history, affecting every aspect of daily life.

Today, most content is still created by hand, the best of it by highly skilled artisans drawing on centuries of scholarship and experience. Recently, we’ve seen significant innovations in social approaches to content creation. But Web 3.0 industrialization takes content manufacturing to an entirely different level. Instead of users manually creating content, machines automate the heavy lifting. Consumers simply push the buttons and get stuff done. Think spinning wheels versus textile mills.

I note in this excerpt the facile leap from content manufacturing to consumers simply push buttons.

The middle man is not expressed. Hmmm. Is Web 3.0 partly about the industrialization of mediation?

Some argue that Web 3.0 will be a leveling force, and proceed to speak of more democratization. Others make wolf-in-sheep-clothing counter arguments. I would tend to wonder how leveling works in the context of the march of capital, and its aims. (But, then, I’ve read too much Ivan Illich.)

thoughts?