Links, Nodes & Bounds

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Niklas Luhmann maintained that his Zettelkasten was his second brain. When it came to researching themes and topics for his numerous books and academic papers, its meta-system of links to discrete ideas and references, too numerous for him to hold in entirety in his own brain, formed a kind of physical, paper-based rather than neural network map of his generalised learning process, from which he could inform and enhance his current thinking. Melvil Dewey, in inventing his Decimal Classification system for cataloguing publications - the heart of most of the world's library catalogues, hinted at a method of cross-connection between discrete sources of knowledge to aid further knowledge generation and research.

Otlet and La Fontaine extended this idea further and established the notion of data connectivity and correlation with the Universal Decimal Classification system, which took Dewey's concept from the simple cataloguing and indexing of documents - taxonomies of books, periodicals and articles, if you will - to a more modern level of ideas-linking: a network of concepts and knowledge. Bill Atkinson, in the mid 1980s, when he came up with HyperCard for the early Macintosh computer, dreamed of a world of easily accessible interlinked ideas rich in multimedia content and with universally-available input, but stopped short of the true potential of his idea, not realising the networked power that would soon be unleashed by Tim Berners-Lee's invention of the World Wide Web in 1991.

In 1985, a few short months after the rollout of the first Apple Mac in the now infamous Superbowl half time advertisement, the now Nobel Laureate Geoffrey Hinton built a Small Language Model, the direct precursor to today's AI and started on his foundational work in our understanding of neural networks. Now working as an independent writer and serving as Emeritus Professor at the University of Toronto, he spends his time lecturing and writing about the the potential pitfalls and dangers of AI and how best to deal with and exploit it for the benefit, rather than to the detriment of its creators. The subtext of his thinking lies generally in the notion that AI is not the actual problem: rather, it's the greed of man in the exploitation of the technology itself that will be the root of all future negative issues with the technology. The future, he says, lies in regulation through public and state pressure being brought to bear on the forces of naked capitalism to use the technology for the better. But I guess that's true of pretty much everything in this twisted world, ain't it?

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