I suspect this might be my last, or near to last, post before the final summary as it’s Friday afternoon, I’ll work on the summary tomorrow, and Sunday (the due date for the Lifestream) is always a crazy day at work for me. It feels really lovely to return to the beginning – back to cyber culture’s questions about what it means to be human, and at the same time to make connections to community (loosely) and to algorithms.
In the video, Ishiguro says that he started the project, building a robot, in order to learn about humans. He asks what it really means to be human – which things that we do are automated? Which things could someone impersonating us do? When are we truly being ourselves, being creative? Erica (the robot) suggests that through the automation of the uninteresting and tedious aspects of life, robots can help us to focus on the creative aspects, and other parts where we are truly ourselves. With AI being essentially the work of algorithms this ties our first block (cyber cultures) to our third (algorithmic culture). Can algorithms allow us to be more human?
In the video it is also asked, what is the structure that underlies human interaction? We can identify many ‘ingredients’ that play a role, but what does it take for a human to interact with a robot and feel that they are interacting with a being? This is from where my – albeit loose – connections to community are drawn. Last week Antigonish 2.0 told 4-word stories about what community means (#Antigonish2 #4wordstory – there were over 700 responses). How will robots understand all these diverse and valuable ways of being together? Maybe they don’t need to – maybe we can – in the spirit of Shinto – accept them as having their own type of ‘soul’, and accept automation of the mundane.. if our economic system allows for and values our very *human* contributions.
Bring on Keynesian theory and the short working week..
Daniela Rus’ presentation was interesting to watch in the context of having recently watched Audrey Watters’ presentation at Edinburgh on the automation of education. Rus doesn’t have the cynicism which Watters (justifiably) has. For example, she identifies an algorithm which is able to reduce the number of taxis required in New York City by 10,000 by redirecting drivers (if the public agrees to ride-share). While this could mean 10.000 job losses, Rus says that, with a new economic model, it doesn’t have to. She describes a different picture in which the algorithm could mean the same money for cab drivers, but shorter shifts, with 10,000 less cars on the road producing less pollution. It’s a solution which is good for taxi drivers, and good for society – but like Watters I fear that within capitalism there is little incentive for commercial entities to make the choice to value people or the environment over profits. Automation should, as Rus suggests in the presentation, take away the uninteresting and repetitive parts of jobs and enable a focus on the more ‘human’ aspects of work, but instead, it can be used to deskill professions and push down wages. Her key takeaway is that machines, like humans, are neither necessarily good or bad. For machines, it just depends on how we use them..
This talk was delivered at Virginia Commonwealth University today as part of a seminar co-sponsored by the Departments of English and Sociology. The slides are also available here. Thank you very much for inviting me here to speak today.
I started out by trying to grab a few select quotes from this talk that Watters delivered at Virginia Commonwealth University in November 2016, but it is pretty much all gold. She writes about how the stories we tell – or have told to us – about technology and educational technology direct the future, and asks how these stories affect decision making within education:
Here’s my “take home” point: if you repeat this fantasy, these predictions often enough, if you repeat it in front of powerful investors, university administrators, politicians, journalists, then the fantasy becomes factualized. (Not factual. Not true. But “truthy,” to borrow from Stephen Colbert’s notion of “truthiness.”) So you repeat the fantasy in order to direct and to control the future. Because this is key: the fantasy then becomes the basis for decision-making.
..to predict the future is to control it – to attempt to control the story, to attempt to control what comes to pass.
Watters’ interrogation of future stories – stories by Gartner, by the New Horizon Report, by Justin Thrun, and others – demonstrate that these stories tell us much more about what kind of future the story-tellers want than about future per se. This matters, Watters suggests, because these stories are used to ‘define, disrupt, [and] destabilize’ our institutions:
I pay attention to this story, as someone who studies education and education technology, because I think these sorts of predictions, these assessments about the present and the future, frequently serve to define, disrupt, destabilize our institutions. This is particularly pertinent to our schools which are already caught between a boundedness to the past – replicating scholarship, cultural capital, for example – and the demands they bend to the future – preparing students for civic, economic, social relations yet to be determined.
It’s a powerful read – and connected to the idea I want to pursue in my final assignment. I’m interested in seeing if there are different stories being told to different segments of the population, and trying to imagine what the consequences of that different imagining might be.
I posted a link to the complete Pew Research Report (Code-Dependent: Pros and Cons of the Algorithm Age) a few weeks back (March 11). This week, while thinking about my final assignment for Education and Digital Cultures, I returned to Theme 7: The need grows for algorithmic literacy, transparency and oversight.
While the respondents make a great deal of both interesting and important points about concerns that need to be addressed at a societal level – for example, managing accountability (or the dissolution thereof) and transparency of algorithms, avoiding centralized execution of bureaucratic reason/including checks and balances within the centralization enabled by algorithms – there were also points raised that need to be addressed at an educational level. Specifically, Justin Reich from MIT Teaching Systems Lab suggests that ‘those who design algorithms should be trained in ethics’, and Glen Ricart argues that there is a need for people to understand how algorithms affect them and for people to be able to personalize the algorithms they use. In the longer term, Reich’s point doesn’t seem to be limited to those studying computer science subjects, in that, if, as predicted elsewhere (theme 1) in the same report, algorithms continue to spread, more individuals will presumably be involved in their creation as a routine part of their profession (rather than their creation being reserved for computer scientists/programmers/etc.). Also, as computer science is ‘rolled out’ in primary and secondary schools, it makes sense that the study of (related) ethics ought to be a part of the curriculum at those levels also. Further, Ricart implies, in the first instance, that algorithmic literacy needs to be integrated into more general literacy/digital literacy instruction, and in the second, that all students will need to develop computational thinking and the ability to modify algorithms through code (unless black-boxed tool kits are provided to enable people to do this without coding per se, in the same way the Weebly enables people to build websites without writing code).
In week 11, I’ve consciously tried to wind back or wind down the Lifestream a little. I understand it is still assessed at this point – but by wind down I don’t mean stop. Rather, I mean ‘refocus’. I’ve tried to be selective about the content I’m feeding in, in order to focus on the assignment, and just feed content associated with that. It’s hard though, as in a sense adding to the Lifestream has supplanted my old habits of storing ‘to read’ texts elsewhere, and telling ‘the story of’ digital cultures through the Lifestream (and my own attempts to subvert algorithms that may be at play) has actually become quite addictive.
That said, I’ve been pursuing two themes in relation to the final assignment: the way that ‘imaginaries’ help create (educational) futures, and the notion of ‘algorithmic literacy’ and the potential to develop this. There’s been more of the former in the feed, but in part I think this may be just because it is better represented in media and research – I’m not convinced that the latter is the less worthy route to pursue.
Here are the ways I’ve been thinking about those themes, and some other bits of ‘life’ that have appeared in the stream:
I’ve been a little distracted the last couple of days, as I’m presenting the paper I wrote for my final assignment for Digital Education in Global Contexts (Semester B, 2015-16) at a conference today. To be fair, a lot of the conference seems focused on the promise technology is perceived to hold for education (I’m thinking of Siân Bayne’s 2015 inaugural lecture, The Trouble with Digital Education, 8:20) and I’m not certain that my paper will be of a great deal of interest to the audience, but it is, nonetheless, a little nerve wracking. As a consequence of over thinking it, no doubt I’ll also be summarising week 11’s lifestream and adding metadata later tonight.
Another exploration in the pursuit of the idea of ‘imaginaries’ and how these fictions play a generative role in the culture of technology – and specifically (my interest rather than that of the article) in education.
ABSTRACT: Scholarship in the history and sociology of technology has convincingly demonstrated that technological development is not inevitable, pre-destined or linear. In this paper I show how the creators of popular films including science consultants construct cinematic representations of technological possibilities as a means by which to overcome these obstacles and stimulate a desire in audiences to see potential technologies become realities. This paper focuses specifically on the production process in order to show how entertainment producers construct cinematic scenarios with an eye towards generating real-world funding opportunities and the ability to construct real-life prototypes. I introduce the term ‘diegetic prototypes’ to account for the ways in which cinematic depictions of future technologies demonstrate to large public audiences a technology’s need, viability and benevolence. Entertainment producers create diegetic prototypes by influencing dialogue, plot rationalizations, character interactions and narrative structure. These technologies only exist in the fictional world – what film scholars call the diegesis – but they exist as fully functioning objects in that world. The essay builds upon previous work on the notion of prototypes as ‘performative artefacts’. The performative aspects of prototypes are especially evident in diegetic prototypes because a film’s narrative structure contextualizes technologies within the social sphere. Technological objects in cinema are at once both completely artificial – all aspects of their depiction are controlled in production – and normalized within the text as practical objects that function properly and which people actually use as everyday objects.
At this point, I have to be totally honest and admit I haven’t got round to reading this yet. It looks as though it could shed light on the intricacies of how fictions influence reality, of how imaginaries can work as construction tools. I hope to get time to read it more closely this week – but it’s a busy, busy week..