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1
Introduction
Why a theory of posthumanist learning? Why now? This book proposes that we need a theory of how material words and material things are entangled through a process that we might formerly have understood as humanistic learning – with an underlying assumption of an individual learner that learns in separation from a material environment. However, what if the humanistic conception of learning is wrong? Maybe we are not learning as individuals separated from our social and material surroundings. Rather, the posthumanist perspective I explore understands the concept of learning from the perspective of the human as an ultra-social collective of social and material collectives. The learning, which connects us in collectives, shapes materials and concepts, just as materials and concepts shape collective learning processes.
Learning has always been a confusing concept to work with because it is used in a multiplicity of ways in different scientific and professional vernaculars (e.g. Scott & Hargreaves 2015). Posthumanist learning may add to this confusion, but also points in a new direction – towards a radically transformed conception of what it is to be human. “As naturalcultural hybrids proliferate, Homo, the conventional subject of anthropological concern, is no longer a clearly bounded biological subject” (Kirksey & Helmreich 2010, 556). This move does not entail that we give up understanding humans as learners, but that we understand learning as a pivotal process for how materials, including human bodies and words, have an entangled agency in a world that matters to humans.
In the learning sciences, learning is often defined as a change that denotes some kind of process. So much can be agreed on, but there are many different approaches to explain what kind of process and what kind of change occurs. Though the concept of learning has reached a popularity that is overwhelming in education (e.g. Biesta 2010), there is no agreement on how the concept of learning should be defined.
One of the difficulties in the study of learning is the lack of an established discipline. Contrary to disciplines like physics or chemistry, the learning sciences do not work from a paradigm, where an agreed framework of ordered thoughts are responded to and selected among the ideas that are communicated. Nevertheless, a largely unquestioned understanding of “the human” as an individual detached from a material world underpins the Western learning sciences (Snaza & Weaver 2015).
It is this view of the human, which is about to change. Posthumanists deconstruct and decentre the rational, intelligent, stand-alone human and emphasise material aspects of agency in the new posthumanist theories. However, few posthumanists have looked into the implications for learning theory when we take a look at what humans bring to bear in a world of material agency, which, after all, has been named “the Anthropocene” (Zalasiewicz et al. 2010). If learning is understood as a basic process, and not just of importance for education, we need a more thorough understanding of how learning constitutes human agency and perception in posthumanist theories. The posthumanist learning theory I propose denotes a process where materials come to align our collective memories and motives, which again inform our embodied perceptions of materials within a phenomenal world. When humans align with materials, new collective agencies in a material world and cultural materialisations follow.
At the core of this alignment process of learning, we find human concepts – as material words that connect us collectively with material things. The concept of “concept” is a topic I take issue with throughout the book (especially in Chapters 5, 6 and 7). As material words, concepts have material agency when expressed, and this further affects the learning processes that transform how we perceive materials and behave with materials in our surroundings. Concepts precede thinking in the psychology of Vygotsky (Derry 2013, 112). From an anthropological point of view, concepts are cultural because human collectives differ in their perception and material engagements. Furthermore, these collectively shared materialised concepts are embodied in culturally localised human bodies. These bodies change as humans engage with materials or as materials engage with humans (I discuss these topics from a postphenomenological perspective in Chapters 8 and 9).
In the learning sciences, the transformation of “human”, in a humanist sense, to “posthumanist human” has practical implications for teaching, but education is not my topic. Learning in education is one way to understand posthumanist learning. Posthumanist learning is, in the present discussion taken up in this book, a general theoretical set of insights we can gain when the new view of the human is discussed up against claims about “being human” and “learning” made in robotics, AI (artificial intelligence) and transhumanism. Thus, the landmarks in robotics and AI become a fertile ground for exploring how machines and humans merge and differ. What matters is the exploration of the relations (Ihde 2002, Rosenberger and Verbeek 2015). When claims are made about how humans can be improved through technology, that machines can learn as humans do and may even become more intelligent, I see these claims as founded on humanist perceptions of the human as a learner. In the last chapter of the book (Chapter 10), I discuss what implications posthumanist learning have for such claims made in AI and robotics.
Posthuman or posthumanist?
The terms posthuman and posthumanism have caused quite a stir in academia. Some reject these terms outright and find them unhelpful because they believe they refer to a rejection of humans, which would be absurd. Some emphasise, with tongue in cheek, that humans have always been posthuman (Hayles 1999). Others (e.g. Fukuyama 2002) reject the term because it denotes an unwanted, improved human merging with machine, as argued by transhumanists like Max More and Natasha Vita-More (More & Vita-More 2013) and some engineers (e.g. Kurzweil 2005). Others see nothing new in humans merging with materials because humans are what Andy Clark (2003) calls “natural born cyborgs”.
At this point an important distinction must be made to clarify my take on these debates. The technical posthuman is, on the one hand, a fictional or literal figure that can be understood as the gradual cyborgian merger of humans and machines (including AI and robotics). On the other hand, posthumanist theories propose new understandings of what humans are and can become.
The technically based posthuman could for instance refer to a transhumanist stage where humans gradually merge with machines until the merger reaches a singularity, where human intelligence is surpassed by incomprehensible machines (Kurzweil 2005). I call this a posthuman theory because it does not question the “human” in the merger. The taken-for-granted human in technical posthuman endeavours is most often the liberal, intelligent human found in humanist theories – which are opposed by posthumanist theories. Some posthumanist theories propose that humans have always been posthuman because we have always merged with our surroundings (Hayles 1999). Others have a more radical anti-humanist stand (Ferrando 2013). Some posthumanists among the social scientists are specifically not interested in enhancing humans – and some even want humans to disappear altogether. They welcome a future where non-humans and materials can live in peace on the planet without humans – because humans appear to be so greedy and unintelligent (see, for instance, Braidotti 2013). However, most posthumanist theory does not necessarily exclude that humans continue to exist. Nor does it exclude that humans are improved, as in transhumanism. The “post” rather rejects a particular theoretical understanding of what hitherto has characterised unquestioned assumptions of humankind.
“Posthumanist”, as I use the term, does not entail that we leave behind a concern for humans, but that we open up for new ways of understanding humans in a material world. This posthumanist world cannot avoid entangling human collectives with materials through learning. Posthumanist learning challenges the humanist understanding of the autonomous being found universally to be the same (as if culture does not matter) and the idea that all humans effortlessly can self-direct their own learning (e.g. Knox 2016, 28) without considering how matter and collectives matters.
Part of robotics, computer sciences and AI evolve in an effort to imitate or emulate human learning (Russell & Norvig 2010). My posthumanist approach emphasises that when machines try to emulate human learning, they seem to run into unsurmountable problems precisely because they build on an understanding of human intelligence as autonomous, rational and universal. However, I also depend on the engineers to develop my concept of human learning. As the technical sciences develop, the engineers dive deeper and deeper into the complexity of making machines perform tasks that most humans have learned to perform effortlessly. The complexity of human learning emerges with a new awareness of our limited understanding of humans as learners, with every advance in the technical sciences.
Machine learning has steadily evolved since the 1950s, and computers like Deep Blue, Watson and AlphaGo have beaten humans in intelligent games like chess, Jeopardy and Go since long ago. Nevertheless, the perception of a notebook, a door, a cup, throwing a ball, walking up a stairway, walking across the floor to pick up a cup and place it in a dishwasher, are all huge challenges that machines only gradually have learned to perform – and they do not perform these tasks as humans would. There is still a long way to go before we shall have robots performing as humans, and maybe we never will (see Figure 1.1).
FIGURE 1.1 Children and some robot makers envision a future where robots behave like humans. Sorine, age 11, for instance has drawn the robot Jens drinking oil, while watching TV. (Photo taken by Cathrine Hasse during experiments with children drawing robots in 2015.)
Human “intelligence” seems to differ radically from machine intelligence. Simple tasks like having a conversation about the weather with a robot makes it clear that talking to a human is not like talking to a robot – unless the robot is allowed to set the premises for the conversation and mainly deliver the facts of the weather forecast.
Vignette 1.1: Jibo’s “empty curiosity”
Jibo is a charming creature: a small, white and black, elegantly designed robot made to stand on a table and communicate with humans. It was launched as the “first social robot for the home who looks, listens and learns” and his homepage also describes Jibo as a “personality” that “shines in everything he does. From his dance moves to his jokes, his charming disposition and unique character makes each interaction special, helpful and surprisingly human”. In November 2017 Jibo made it to the front page of Time magazine as one of the 25 best inventions that year. The robot was the brainchild of the M.I.T.-based robot maker Cynthia Breazeal, one of the world’s leading engineering robot-makers (see www.jibo.com). In her 2002 book Designing Sociable Robots she defined social robots as having a personality: “For me, a sociable robot is able to communicate and interact with us, understand and even relate to us, in a personal way. It should be able to understand us and itself in social terms” (Breazeal 2002, 1).
Jeffrey Van Camp, a reviewer for the magazine Wired, tried to introduce Jibo into his family in 2017; however both he and his family soon became disappointed. Even though the new family member quickly learned the names of the Van Camps, contributed to conversations with facts (sometimes even funny facts), remembered the family members’ birthdays and said “Congratulations” on the day, the family increasingly grew tired of Jibo. Van Camp explained, in a review in 2017, that Jibo did not seem to learn much, and furthermore the Van Camps had expected real, humanlike engagements with the social robot. Instead, it began to feel uncanny.
“For my wife, Jibo’s empty curiosity started coming off as invasive”, Van Camp explained. She felt watched over in the kitchen as Jibo’s camera eyes seemed to stare at her. But even worse – the human engagement through learning was lacking. As time went on, Van Camp recounts, his wife began to see the promised sociality of Jibo as an unfulfilled promise.
She felt like Jibo was deceiving her: ‘I guess I thought it was following me everywhere because it was learning, but he’s not learning anything,’ she told me one night. ‘He says he’s learning but he’s not. I thought he was gonna be cute, but he won’t stop staring at me.’ Worse, I couldn’t tell Jibo to stop staring at my wife. He didn’t understand the question, and i...
