The claim that we now live in a consumer society has become commonplace in academic research. People have become voracious consumers, but also present themselves as desirable commodities for the consumption of others. At the same time, it is increasingly argued that contemporary societies are becoming more automated and accelerated as robotic devices augment many aspects of work, space and everyday life. In smart cities, for example, urban spaces are set to become more automated as networks of sensors and data analytics continually gather data about systems and movements of people and objects to become more ‘sentient.’ These sentient cities are structured and supported ‘line by line, algorithm by algorithm, program by program,’ ‘by code using data as fuel’ as Nigel Thrift has argued. In education, the ‘robotic algorithms’ of learning analytics are also beginning to play a greater role in automating aspects of the pedagogic process, as the authors of Learning with Big Data argue, through accessing spreadsheets of learner data, calculating odds and making probabilistic predictions, in order to automate decisions about pedagogical intervention in a few milliseconds. Some aspects of education policy processes are even being delegated to automated analytics software which is programmed with the capacity to consume educational data and produce specific insights from it. In these examples, we can see how robots play a key role and how they require dietary sustenance to function–‘fuel’ as indicated above, or better perhaps, ‘food.’ So if we are indeed living in a consumer society, it is one in which robots and not just humans have a voracious and greedy appetite.
Image: Chris Isherwood
The question of what robots eat is deliberately playful. But the underlying issue is a significant one. It’s about the ways in which contemporary digital data analytics systems feed on a diet of data produced through human activity. Through this feeding, robotic machines receive the informational nutrition required for their own development: to become smarter, more aware of their environment, more responsive and adaptive in their interactions with people.
I have been working recently on a project involving health scientists, an anthropologist and a robotician to develop a prototype device for the collection of dietary and nutritional information from children. The device consists of a couple of technical elements: a wearable smartwatch and a portable robotic toy. As children go about their day-to-day activities, the smartwatch device encourages them to monitor and track what they eat and drink. Later, when the smartwatch is brought into proximity with the robotic toy, it then ‘feeds’ the robot all of the data it has collected on the user’s dietary habits and nutritional intake that day. The robot is then intended to respond to the user’s input by playing with them. Future iterations of the device could also monitor children’s physical activity; specific algorithms could be designed to work out the optimal balance of calorific intake with physical movement and exertion. There is much in it that resonates with the ‘quantified self’ health-tracking movement, and particularly with how such devices are being mobilized for the algorithmic tracking of children’s physical exercise and for encouraging healthy lifestyles.
The twist with this device, though, is that by enabling Bluetooth connectivity between the smartwatch and the robot, we are building in a specific relationship between the user and the machine. The user is becoming responsible for feeding the robot. We’ve been considering the ways in which children interact with ‘virtual pets,’ often by feeding and nurturing them to ensure their well-being. Building on similar principles for the big data context, as our little health robot is fed data, it is designed to gradually develop a series of increasingly complex interactions with the user. Though currently no more than a set of lashed up prototypes, the ultimate aim of the project might be to create a robotic health assistant that can learn about the user’s dietary and physical habits, interact with the user, and even intervene to reshape those health-making routines. We’re nowhere near that level of sophistication now; the project is a kind of conceptual development to help think through the social and technical possibilities, potentials and problems of robot interaction in the dietary data collection process.
The educational questions here concern the ways in which devices can be designed to learn about children’s health in order to support children themselves to learn about their own healthiness, or even to act pedagogically to teach children about healthy activities and lifestyles. At the moment, and a little more prosaically, we’re thinking about the device as a tool for engaging children in the capture of nutritional data, a notoriously challenging methodological task in the health sciences. But even as a data collection device, it might still be seen as a prototypical robotic research assistant—a device that hangs out ethnographically with its user and details the minutiae of its consumption and exertion patterns. There is considerable current methodological interest in mobile and ‘smart’ web-based and wearable devices for quantitative, qualitative, and mixed-methods research design.
What interests me more here is the possibility of exploring the connections between what we feed into our bodies as human beings and how that might then be translated into data that we can feed to robots–and specifically the interaction between children capturing data about their own bodies and feeding it into the body of a robot. As researchers involved in the Eating Bodies project such as Annmarie Mol have suggested, the way we eat is largely governed by questions about what we should eat, in terms of calories or pleasure, and how much to eat, in terms of nutrients and satiety. What we eat can even be understood as incorporated into our bodies to become part of ourselves. Robotic health assistants could play a major role in governing how and what we eat, and what we ultimately make of ourselves as eating bodies as a consequence.
But an equally intriguing issue might be about what robots eat, how much of it they eat, and how it becomes incorporated into their own ‘bodies.’ We could think here about how robotic algorithms, such as those emerging from the field of machine learning or even cognitive computing, are able to turn such data into the kind of artificial nutrition required for their own development. Machine learning algorithms already fundamentally need to be ‘taught’ by being trained on a corpus of available data before they can go to work ‘in the wild,’ where they can then further learn through constant interaction with they data they encounter. Likewise, the robotic health assistant needs to be fed on a diet of data that is captured in real-time from its user, and that could then use that data-diet to learn about the user’s own bodily habits. In a way, this is about the data-hungry algorithm devouring and digesting the digital traces left by human activity, and then using the intelligence gained through that diet (and incorporated into the ‘body’ of the algorithm itself) to reshape the body techniques of its user. There are links between eating bodies and eating robots to consider.
The robotic health assistant project is just one instantiation here of the growing recognition of the productive power of algorithm systems to interact with and reshape humanly embodied actions and routines. Contemporary consumer societies are characterized not just by patterns of human consumption, but by the ways that robots are now being designed as consumers of human data. But in the same way that contemporary consumers are also encouraged to become producers of digital content—by uploading social media content, updating profiles, liking and commenting and so on—our robots are also deeply productive machines. They, like us, are ‘prosumers‘, or ‘prosuming machines‘ as George Ritzer claims, who both consume and produce media. If, as consumer culture theorists claim, we now present ourselves as commoditized products to be consumed, it’s not just for the consumption of other people but for consumption by robots. In relation to food consumption specifically, we even present our ‘eating bodies’ in relation to algorithmic standards structured into health-tracking devices such as smartwatches. Eating robots consume the data from our eating bodies and turn it to productive action. Trained as consumers and producers by the technical experts designing them, eating robots may be reproducing our own eating bodies. As automated dietary assistants and health devices become more widespread, the boundary between practices of producing food to feed ourselves and practices of producing data to feed robots could become more blurry, with robots increasingly responsible for producing the data required to educate us to produce the food that will optimize our nutrition, pleasure and satiety as consumers.