Robbie the Robot generates 480 pints of whiskey overnight
Why aren’t geographers talking more about robots? This question struck me, paradoxically, as I sat on a panel on robots at the last AAG (see del Casino Forthcoming). While this might seem the last place to have this thought, it was prompted by two things. First, the smiles of slightly startled amusement from people when I told them I was on a robot panel, and second, my co-panelists, who I thought were missing out some important terrain about robots.
Putting aside the no doubt justifiable bemusement that the AAG had a robot discussion, the other topics discussed that day dwelt on sexbots, love dolls, cyborgs and the more-than-human. These are part of, but not the whole story, as Rosi Braidotti’s recent book on posthumanism documents (also putting aside how/if more-than-human is different from post- or transhumanism).
For me, the latter are cultural or philosophical issues, and no matter how pertinent and interesting, they leave aside the political-economic, which is what I’m interested in here. Vinny’s piece (which has just been released by PiHG online first) does partially offer to take up this issue. He does this in the context of a report on social geographies, perhaps meaning that the economic and political are marginal for his piece, which nevertheless remains required reading.
What I mean is quite simply issues around automation, artificial intelligence, and computerization. For me, these point to one thing: algorithmic life. One big part of this is the effect on jobs and wages, and therefore we need to do a better job of integrating tech with geographies of the economy.
Or what I called on the panel “Geographies of neoliberal robots.” Everyone probably has seen a version of this graph:
The productivity-wage gap
A version appears in Harvey’s book on neoliberalism. The point is that since the advent of the neoliberal era (say 1970s and early 80s) productivity has not failed to climb, but the amount returned to workers has stayed about the same, creating a productivity-wage gap, which in turn widens income inequalities.
As I said on the panel:
Two explanations are usually offered: “robots ate all the jobs” (people are put out of work by automation), or a deliberate political project by a revanchist capitalist elite (Harvey).
These explanations are not mutually exclusive. What is interesting is that automation and robots may no longer be occurring in only unskilled and repetitive jobs. Research suggests jobs that are more routine and less “cognitive” are the most susceptible to automation. A well-known 2013 study at Oxford Martin School estimated that nearly half (47 percent) of US jobs are at risk of automation. Geographers are not immune:
Source: NPR/Oxford Martin School, Univ. Oxford
Things we can do
- Given that we have this listing of job susceptibility, it would be nice to get at least a baseline map of where jobs are at stake. How about a county-by-county map of potential automation? Take all jobs per county and multiply them by the relevant figures in the study. It wouldn’t be perfect but would give us a baseline map.
- The PC was Time magazine’s “machine of the year” in 1982. But a one-for-one replacement of a human job with a computer job need not be the most important development in automation or intelligent machines. Rather, production may undergo wholesale reorganization. (Brynjolfsson and McAfee make this point in their recent book The Second Machine Age.) Geographers can contribute to our understanding of this by analyzing which industries are susceptible, and where they are located.
- Turning to computerization and automation, I mentioned above that these evidence algorithmic life. What I mean by this is very simple, if you follow Tarleton Gillespie’s definition of the algorithm:
they are encoded procedures for transforming input data into desired output, based on specified calculations (Gillespie 2014: 167)
Notice here three useful points: encoding, desire, calculation. An algorithm is that which enables desire to proceed by making (performing) the world as calculative. So it is a capacity-making. Here there would be plenty to look at in terms of uneven geographical outcomes of the work algorithms do in the world, for example on tracking and geosurveillance.
In fact, Rob Kitchin and his group have just published a useful listing of the ways this occurs. One example likely to be of interest to geographers is automated facial recognition. I really think we need to think “beyond the smartphone” as the only way we are tracked to include ALPR, gait observance, wearable devices/Fitbits/smart watches, and Minority Report style live biometric tracking (face|iris|gait). I document some of these in my piece “Collect it All” as does Leszczynski in her “Geoprivacy” overview.
- Beside being part of algorithmic governance, drones (and I include commercial drones especially here as they are predicted to far surpass military drone spending) could be an object of geographical enquiry, or what I call “the drone assemblage.”
- Read Vinny’s piece for a more general overview of many aspects of robots and intelligent machines.
“Where have you been? It’s alright, we know where you’ve been!”–Welcome to the Machine, Pink Floyd