Post Category → Tools, Technology and Internet

The Power of Networks

A new video from the lovely RSA Animate team, this time on the Power of Networks.

Introduced me to the concept of “Rhizome“:

Gilles Deleuze and Félix Guattari use the term “rhizome” and “rhizomatic” to describe theory and research that allows for multiple, non-hierarchical entry and exit points in data representation and interpretation. In A Thousand Plateaus, they oppose it to an arborescent conception of knowledge, which works with dualist categories and binary choices. A rhizome works with planar and trans-species connections, while an arborescent model works with vertical and linear connections. Their use of the “orchid and the wasp” is taken from the biological concept of mutualism, in which two different species interact together to form a multiplicity (i.e. a unity that is multiple in itself). Horizontal gene transfer would also be a good illustration.

As a model for culture, the rhizome resists the organizational structure of the root-tree system which charts causality along chronological lines and looks for the original source of “things” and looks towards the pinnacle or conclusion of those “things.” A rhizome, on the other hand, is characterized by “ceaselessly established connections between semiotic chains, organizations of power, and circumstances relative to the arts, sciences, and social struggles.” The rhizome presents history and culture as a map or wide array of attractions and influences with no specific origin or genesis, for a “rhizome has no beginning or end; it is always in the middle, between things, interbeing, intermezzo.” The planar movement of the rhizome resists chronology and organization, instead favoring a nomadic system of growth and propagation. In this model, culture spreads like the surface of a body of water, spreading towards available spaces or trickling downwards towards new spaces through fissures and gaps, eroding what is in its way. The surface can be interrupted and moved, but these disturbances leave no trace, as the water is charged with pressure and potential to always seek its equilibrium, and thereby establish smooth space. – Wikipedia

Algorithm wars and ultrafast machine ecologies

In an amazing 2011 TED talk, Kevin Slavin explained how “algorithms shape our world”.

Its well worth a watch to prepare you for the the main part of this post, an analysis of Black Swan events  in financial markets caused by exactly the sort of algorithms Slavin describes.

From “Financial black swans driven by ultrafast machine ecology”:

Society’s drive toward ever faster socio-technical systems, means that there is an urgent need to understand the threat from ‘black swan’ extreme events that might emerge. On 6 May 2010, it took just five minutes for a spontaneous mix of human and machine interactions in the global trading cyberspace to generate an unprecedented system-wide Flash Crash. However, little is known about what lies ahead in the crucial sub-second regime where humans become unable to respond or intervene sufficiently quickly. Here we analyze a set of 18,520 ultrafast black swan events that we have uncovered in stock-price movements between 2006 and 2011. We provide empirical evidence for, and an accompanying theory of, an abrupt system-wide transition from a mixed human-machine phase to a new all-machine phase characterized by frequent black swan events with ultrafast durations (<650ms for crashes, <950ms for spikes). Our theory quantifies the systemic fluctuations in these two distinct phases in terms of the diversity of the system’s internal ecology and the amount of global information being processed. Our finding that the ten most susceptible entities are major international banks, hints at a hidden relationship between these ultrafast ‘fractures’ and the slow ‘breaking’ of the global financial system post-2006. More generally, our work provides tools to help predict and mitigate the systemic risk developing in any complex socio-technical system that attempts to operate at, or beyond, the limits of human response times.

As a great friend of mine, who happens to be a banker who is very senior in the markets, once commented to me by mail, talking about how trading was accelerating into millisecond cascades as described by this paper:

slow the fck down everyone

Robots and software really are eating jobs

Following on from my post last night (“Robots and the Chinese“), I received a great link in the mail this morning.

From the Economist, “Difference Engine: Luddite legacy“:

But here is the question: if the pace of technological progress is accelerating faster than ever, as all the evidence indicates it is, why has unemployment remained so stubbornly high—despite the rebound in business profits to record levels? Two-and-a-half years after the Great Recession officially ended, unemployment has remained above 9% in America. That is only one percentage point better than the country’s joblessness three years ago at the depths of the recession.

…The conventional explanation for America’s current plight is that, at an annualised 2.5% for the most recent quarter (compared with an historical average of 3.3%), the economy is simply not expanding fast enough to put all the people who lost their jobs back to work. Consumer demand, say economists like Dr Tyson, is evidently not there for companies to start hiring again. Clearly, too many chastened Americans are continuing to pay off their debts and save for rainy days, rather than splurging on things they may fancy but can easily manage without.

There is a good deal of truth in that. But it misses a crucial change that economists are loth to accept, though technologists have been concerned about it for several years. This is the disturbing thought that, sluggish business cycles aside, America’s current employment woes stem from a precipitous and permanent change caused by not too little technological progress, but too much. The evidence is irrefutable that computerised automation, networks and artificial intelligence (AI)—including machine-learning, language-translation, and speech- and pattern-recognition software—are beginning to render many jobs simply obsolete.