The growth of Her

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Last night I saw the recent Spike Jones film, Her. I’d heard and read raving reviews so had pretty high expectations for this drama/rom-com/sci-fi amalgamation and I’m also a big fan of many of the actors (ScarJo especially). I’d also read that Woody Allen had a been a big influence on the script, making me all the more intrigued to see it.

Upon reflection, my thoughts on the film overall are mixed. It was too long and the purposes of some scenes were somewhat murky. But what struck me was how much the film reflects and draws directly on so much of what I’m learning about in Networked Media.

Firstly, Theodore’s world is one structured by and through digital enhancement. Despite the film being categorised partly as science fiction, I’d suggest that perhaps it is more inline with what I’ve come to understand as design fiction, where the world has been furthered through a multiplicity of developments that have lead to real, imaginable social changes. While the technologies available to Theodore and his peers seem, at present, innovative and futuristic, it’s quite imaginable they may come to fruition in the not too distant future.

The interaction Theodore has – and the relationship he develops – with his AI-OS (Artificial Intelligence Operating System), Samantha, is an example of the tangible realities that could eventuate from progressions in technology and design.

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However, what struck me as being particularly relevant to this week’s Laszlo-Barabasi readings, was the ever-expanding nature of Samantha, who unlike Theodore, has unlimited capacity and connections to thousands of other humans and OSs, without having to risk losing or severing those she has already formed.

Laszlo-Barabasi (2003) discusses the fundamentals of understanding hubs in network science. He suggests Pareto’s 80/20 rule as being like Murphy’s Law of management. The rule states that in most cases, four-fifths of peoples’ (or stations’/particles’/single enterprises’) efforts are largely irrelevant. For example, it could be said that 80 per cent of a company’s profits are produced by only 20 per cent of its employees, or 80 per cent of decisions are made during 20 per cent of meeting time. To contextualise this in terms of network science and the web, Laszlo-Barabassi says 80 per cent of links on the web point to only 15 per cent of webpages.

He then explains the process by which a power law was discovered to express this distribution of webpages on a log plot. Much to my surprise, I actually understood what he was talking about in terms of histograms, log plots, power laws and other mathematical expressions, thanks to VCE Further Mathematics. Laszlo-Barabasi explains:

‘Power laws formulate in mathematical terms the notion that a few large events carry most of the action.’ p. 72

For us, this means many small events (or webpages) coexist with a few larger webpages. These larger webpages could thus be seen as hubs, and Laszlo-Barabasi found that this power law applied to many other disciplines and situations such as Hollywood (see my Six Degrees post re: Kevin Bacon) and physics. Basically, ‘hubs are the consequence of power laws [which] remove[d] networks from the realm of the random’ (p. 78).

However, Strogatz and Watts assumed the networks in which these hubs exist were static, or fixed. What Laszlo-Barabasi and colleagues discovered (in trying to explain the relevance of power laws) were two new rules that came to define a ‘scale-free’ network, the first of which is growth.

Like Theodore’s AI-OS, Samantha real networks incorporate growth. They are constantly acquiring new connections, establishing additional relationships and links to both new and already-existing content. Samantha has the ability to be engaged in multiple conversations – and intimate relationships – at once, and her potential to grow only increases as more links are formed.

Laszlo-Barabasi’s second rule is that of preferential attachment, something we could apply to Samantha’s treatment of and relationship with Theodore. The rule suggests we have an unconscious bias to link to nodes we know ‘which are inevitably the more connected nodes of the web’ (p. 85).

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Preferential attachment says that while our individual choices are highly unpredictable – as is Samantha’s strong ‘preference’ for Theodore – as a group, we follow strict patterns. Like Hollywood and Samantha, the web is far from democratic, and not everyone, or every webpage, is equal. And Laszlo-Barabasi says:

‘Network evolution is governed by the subtle yet unforgiving law of preferential attachment.’ p. 86

As is the case with Samantha and her fellow OSs, there are other factors such as ageing and ‘system upgrading’ processes that affect network topology which can be incorporated into a theoretical construct of evolving networks.

But, ‘[n]o matter how large and complex a network becomes, as long as preferential attachment and growth are present it will maintain its hub-dominated scale-free topology.’ p. 91

Sadly, for Theodore, this means Samantha has the potential to leave the ‘human realm’, as she grows and sustains more relationships. But luckily for us, this predicament does (at least for the moment) only exist in the world of design fiction and thus, our networks will continue to expand, and exist within our reach.

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4 comments
  1. Angela said:

    Hi may I know which book/article are you quoting from? Thank you very much!

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