I’m not sure I believe in coincidences. I’ve always been fascinated by the similarities I’ve found between people, and things, and animals and art and cultures and belief systems. The older you get, the more these similarities jump out at you. So this is a lecture about similarities, coincidence and connectivity – with specific emphasis (of course) on digital media and that might digress into other strange areas that preoccupy me.
HUBS AND MEANING-MAKING
We might not realise it but we are all part of a network. Even if we largely keep to ourselves we use networks to navigate the world. Roads, trains, telephones, computers, friends, associates – everything basically from the structure of our brain and arterial system in our bodies to the way we communicate and learn – is part of a network. Networks are powerful structures with the emphasis on interconnectivity and this interconnectivity gives us the ability to learn.
In fact did you know that:
“Life and mind have a common abstract pattern or set of basic organizational properties. The functional properties characteristic of mind are an enriched version of the functional properties that are fundamental to life in general. Mind is literally life-like.”
Godfrey-Smith, P. (1996). Complexity and the Function of Mind in Nature. Cambridge: Cambridge University Press.
“Mind is literally life-like. The Universe and Life are literally mind-like. “
Peter Winiwarter (2008). Network Nature. www.bordalierinstitute.com
Of course we had maps and plans and ways of systematizing information well before we understood about neural networks or had the Internet to study – but the earliest instance of modern complex network theory can be found in the work of Ludwig von Bertalanffy from the 1940s. His General Systems Theory (an attempt to elucidate principles that can be applied to all types of systems at all nesting levels in all fields of research) was part of his grand project to map hard sciences, social sciences and humanities, technology and art. He employed a set of descriptive notions:
These notions and concepts, in turn, were passed on to those taking a radical constructivist stance (scientific knowledge is constructed by scientists and not discovered from the world) and then lead to Chaos theory (the butterfly effect – small differences in initial stimuli can have global effects) which in turn fed into cybernetics (the study of systems that have ‘goals’).
As network theory itself began to take shape in the latter part of the 20th century it was discovered that networks can take different shapes. The two distinct types being scale-free networks (which are the most pervasive) and random networks (less common and less stable).
Scale-free networks are characterised by what’s known as the power law distribution and the clustering coefficient distribution. The power law distribution allows for a fault-tolerant design as:
major hubs are closely followed by smaller ones. These, in turn, are followed by other nodes with an even smaller degree and so on. This hierarchy allows for fault tolerant behaviour in the face of random failures: since the vast majority of nodes are those with small degree, the likelihood that a hub would be affected is almost negligible.
The structure of scale-free networks are likened to the structure of the Internet because of these decreasing sized hubs and clusters.
What conforms to this scale-free model?
- Scale-free networks are pervasive in biology (watch slime mould solve puzzles)
- Scale-free networks are pervasive on the Internet
The video below is a TED talk by Henry Markram – director of the Blue Brain Project – an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level.
Scale-free networks contain components with a highly diverse level of connectivity. Some components form highly interconnected hubs, while other components have few connections, and there are many levels of interconnectivity in between. Scale-free networks are pervasive in biology. Computer simulations at the University of Chicago show that scale-free networks are able to evolve to perform new functions more rapidly than an alternative network design.
What’s all this got to do with Digital Media?
Until the advent of the Internet, network theories were concerned with the interfaces I’ve mentioned above. There was no example of a global network that might operate in the way the Internet does because it didn’t exist.
Early media theorists enthusiastically adopted McLuhan’s concept of media – perhaps because they’d rather have McLuhan than the cybernetics model (which is more rooted in the behaviourist theories). McLuhan predicted the networked connectivity of Internet in the 1960s by the way. However, McLuhan’s assumption that “the medium is the message” and that machines might have their own agenda meant that the media theorists would be grappling with problems that went beyond behaviourism. Behaviourists believe that psychologists & (social) scientists should only concern themselves with ‘observable behaviour’ and avoid theorising about what happens iside someone’s mind (in terms of their beliefs or thought processes).
Meanwhile the bods at Google are trying to use neural networks to better understand face and speech recognition on the Internet. They too (of course) have realised that the way the human brain is wired is uncannily similar to the way the Internet has evolved and are trying to exploit this understanding to make the Internet even more like our brains.
It can’t be a coincidence that computer systems mimic natural systems that in turn look like our brains and that in turn are a mirror image of the whole universe. Everything is made out of the same stuff after all – but it still fascinates me how every new ‘evolution’ digital or biological happens in the same way. And that the in the evolution of the Internet, we have perhaps, unwittingly, created an Artificial Intelligence in our own image.
The seeking, connection-making imperative that works in our brains (neurons and synapses) seems to be reflected in our digital structures. We are hard-wired to constantly seek out new connections just as we are hard wired to trick ourselves – fill in gaps in our perception and create meaning and narrative from the world around us and the media we use. And in this connected digital world we are somehow striving – on the Internet to make the ultimate brain that does everything our soft wet brain can do.
However as Olga Goriunova from dxlabs maintains, we should always try and remember that:
- feedback is not interaction
- computation is not cognition
- storage is not memory
- data is not knowledge
- telecommunication switches are not social networks
- cybernetic mapping is not the cultural territory.