Image Schemas and Concept Invention: Cognitive, Logical, and Linguistic Investigations
The symbol grounding problem is a prototypical problem in cognitive science and concerns how symbols gain their meaning. In this chapter, the symbol grounding problem is discussed in order to address the missing step for how artificial intelligence research can approach conceptual understanding and concept invention. A potential solution to the problem is offered through the theory of embodied cognition. However, Moravec's paradox states that high level cognition such as calculation and memory require fairly little computer power, whereas low level cognition in the form of sensorimotor processes require substantially more computer power. This means that any computationally embodied system faces challenges for knowledge representations. Additionally, the chapter introduces the state of the art in relevant research on creativity and concept invention from a cognitive perspective in order to lay the foundation for successive chapters. The chapter includes considerations on and discussion of: Artificial life Symbol grounding problem Embodied cognition Knowledge representation Creativity and concept invention Information transfer Conceptual blending 1.1 Setting the Scene 1.1.1 On Creating Artificial Life and Intelligence It was on a dreary night of November that I beheld the accomplishment of my toils. With an anxiety that almost amounted to agony, I collected the instruments of life around me, that I might infuse a spark of being into the lifeless thing that lay at my feet. Mary Shelley, Frankenstein 3 (c) Springer Nature Switzerland AG 2020 M. M. Hedblom, Image Schemas and Concept Invention, Cognitive Technologies, https:/doi.org/10.1007/978 3 030 47329 7_1 4 1 Creating Concepts: Considerations from Psychology and Artificial Intelligence The creation of life is a mystery that has kept the human mind busy since the dawn of cognitive thinking. In most religions there exists a creation story in which a divine spirit (or aliens) brings forth life on earth. In parallel, myths and legends speak of humans giving life to golems and homunculi and literature and pop culture introduce monsters, living dead and robots that may not only take over the world, but become sentient with a will of their own. Perhaps the mystery of life, in particular the desire to create it, lay in the endeavour to build a Tower of Babel and to play God, perhaps it is to create a companion or to build a slave, perhaps it is simply to better understand what we are and where we come from. Regardless of reason, what once were the stuff of dreams and science fiction is now something that modern science slowly is tapping into. The introduction starts with a quote from Mary Shelley's Frankenstein, or The Modern Prometheus, in the scenario in which Dr Frankenstein is about to give life to a lifeless creature. While the likes of Dr Frankenstein might appear comical and ridiculous in a scientific context, biologists can manipulate DNA through CRISPR techniques (Cong et al. 2012) allowing the transfer of genetic properties from one species into another one. Research programs such as these can bypass natural evolution and generate 'new life' through innovative scientific methods. If we accept that biologists are concerned with the physical and biological aspects of generating new life, it can be argued that computer scientists have been responsible for constructing a mind. In the early days of computer science, one goal was to simulate human cognition by generating artificial intelligence. Storing memory and performing calculations were some of the earliest signs of artificial intelligence to emerge through the computer sciences, and today there exist complex computer systems that successfully perform increasingly advanced tasks like face recognition, predicting outcomes in world politics, winning games of chess and Go, trading on the stock market, and recommender systems that introduce you to your next favourite film. Despite the remarkable progress seen in artificial intelligence research and that of computer systems, not least through the development of cognitive computing, one thing that neither biologists nor researchers of artificial intelligence have managed to simulate is the 'human soul. Regardless if you believe in the existence of an actual soul or not, there is one category of cognitive phenomena that still remains an issue for artificial intelligence research to simulate. Some phenomena belonging to this group are emotions, contextual appropriateness, natural language understanding and generation, and creativity, areas undoubtedly connected to personality and the human notion of a soul. Creativity is a particularly difficult field of research as it is an umbrella term for many cognitive processes that are still largely undefined. Naturally, if it is uncertain how human creativity works, it can be argued that the artificial simulation thereof is not any easier. An additional problem is that even the simplest of artificial systems, consisting of only a few lines of code, a few grammatical rules and a database of words, can randomly generate a poem, yet humans are often unwilling to ascribe to this kind of performance any creative ability (Colton and Wiggins, 2012) For creative ability, the presence of something more, something like a 'soul, appears to 1.1 Setting the Scene 5 be required for a human to acknowledge that a product is the result of a creative or innovative process. Perhaps biased by his time's culture and religious views, Descartes proclaimed a classic view of the soul and spoke of the Body Mind Problem. Still today this remains an open issue in philosophy and cognitive science as a whole, namely the relationship between the internal mind and the external body. The problem has been approached somewhat differently through the decades and, arguably, it was rephrased by Harnad (1990) into the Symbol Grounding Problem. This rephrasing allows the researcher to ignore all the problems that arise when trying to define something as abstract, loaded and elusive as a soul, or even a mind, by refocusing the problem to how symbols in the world (e.g. words, signs, pictures and behaviours) gain their meaning. In the next section, this problem is introduced properly. 1.1.2 The Symbol Grounding Problem One of the prototypical problems in cognitive science, linguistics, and artificial intelligence is the symbol grounding problem. Simply put it deals with the question of how symbols acquire meaning. More formally the problem can be summarised as Harnad (1990) describes it: How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their (arbitrary) shapes, be grounded in anything but other meaningless symbols? One of the most famous critiques of the development of (strong)1 artificial intelligence that brought forth the symbol grounding problem, is the thought experiment The Chinese Room introduced by Searle (1980) In his seminal paper, Searle uses an analogy in which a person is isolated in a room in which different symbols enter the room from one direction. The person's purpose is to 'rewrite' these signs following a set of rules before returning them to the outside in another direction. What the person is unaware of is that the symbols actually are Chinese characters constructing meaningful sentences. Thus, by following the instruction rules the person ends up 'communicating' in Chinese. At this revelation, Searle proceeds to ask the reader: "Does the person in the room speak Chinese?" Most of us would probably intuitively take Searle's stance and argue that the person in the room does not speak Chinese because a fundamental part of cognition is missing. One suggestion of what is missing is intentionality2. In this setting, the Chinese characters lack meaning to 1 Proclaimers of strong AI, or Artificial General Intelligence (AGI) as it also is called, defend the proposition that a system is a 'mind of its own' and can be claimed to understand and experience cognitive states. In opposition, weak AI supporters suppose that artificial systems can only 'illuminate' human behaviours. 2 Intentionality is the power of minds to be about, to represent, or to stand for, things, properties and states of affairs (Jacob, 2014) 6 1 Creating Concepts: Considerations from Psychology and Artificial Intelligence the translator. The general consensus to explain this intuition is that symbols do not acquire meaning solely in relation to other symbols. In linguistics, the symbol grounding problem is often discussed in relation to The Semiotic Triangle, or the triangle of reference, see Figure 1.1 (Ogden and Richards, 1989) The semiotic triangle highlights the problem of how the real world referent relates to its symbolic representation and the mental concept. This differs from the symbol grounding problem as it does not abstract away from the mental representation, but include this neuro cognitive domain as well. The view of cognition has undergone many paradigm shifts through the years. The classic view Computationalism or 'cognition is computing' was introduced alongside the birth of computer science through the ideas of Newell and Simon (Gardner, 1985) While this provided excellent growing grounds for the initiation of artificial intelligence research, it proved to be difficult to explain not only the symbol grounding problem within this framework but also the human mind as a whole. It appears as though the human mind does not act in the logical way imposed by computationalism. One group of theories for cognition that has been growing in influence is that build on the Embodied Mind Hypothesis (Shapiro, 2011) In the next section these two views of cognition with emphasis on the embodied mind hypothesis will be discussed as this provides an interesting stepping stone
Cognitive Technologies