No fundamental new ideas have appeared in AI for decades because of a deadlocked discussion between the technologists and their philosophical critics. Both sides claim possession of the one (dogmatic) truth: Technologists are committed to writing code, while critics insist that AI bears no resemblance to how humans cope in the world. The book charts a middle course between the critics and practitioners of AI, remaining committed to writing code while maintaining a fixed gaze on the human condition. This is done by reviving a technique long-shunned in cognitive science: Introspection. Introspection was rejected as a scientific method since 1913, but technology is committed to “what works” rather than to science’s “best explanation”. Introspection is shown to be both a legitimate and a promising source of ideas for AI. The book details the development process of AI based on introspection, from the initial introspective descriptions to working code. This book is unique in that it starts with philosophical (and historical) discussions, and ends with examples of working novel algorithms. The book was originally a PhD thesis. It was edited for book form with two new chapters added.

Central scholars in AI have argued for extending the search for new AI technology beyond the tried-and-tested biologically and mathematically-inspired algorithms. Following in their footsteps, areas in the humanities are introduced as possible inspirations for novel human-like AI. Topics discussed include play-acting, literature as the field researching both imagination and metaphors, linguistics, music, and hermeneutics. In our ambition to reach general intelligence, we cannot afford to ignore these avenues of research.

The field of artificial intelligence (AI) has grown dramatically in recent decades from niche expert systems to the current myriad of deep machine learning applications that include personal assistants, natural-language interfaces, and medical, financial, and traffic management systems. This boom in AI engineering masks the fact that all current AI systems are based on two fundamental ideas: mathematics (logic and statistics, from the 19th century), and a grossly simplified understanding of biology (mainly neurons, as understood in 1943). This book explores other fundamental ideas that have the potential to make AI more anthropomorphic. Most books on AI are technical and do not consider the humanities. Most books in the humanities treat technology in a similar manner. AI and Human Thought and Emotion, however is about AI, how academics, researchers, scientists, and practitioners came to think about AI the way they do, and how they can think about it afresh with a humanities-based perspective. The book walks a middle line to share insights between the humanities and technology. It starts with philosophy and the history of ideas and goes all the way to usable algorithms. Central to this work are the concepts of introspection, which is how consciousness is viewed, and consciousness, which is accessible to humans as they reflect on their own experience. The main argument of this book is that AI based on introspection and emotion can produce more human-like AI. To discover the connections among emotion, introspection, and AI, the book travels far from technology into the humanities and then returns with concrete examples of new algorithms. At times philosophical, historical, and technical, this exploration of human emotion and thinking poses questions and provides answers about the future of AI.

Author Index A Alexandrova, Anna, 117 Astromskis, Paulius, 231 Avin, Shahar, 117 B Banerjee, Shreya, 136 Beckers, Sander, 235 Bhatnagar, Sankalp, 117 Bidabadi, Golnaz, 40, 190 Bringsjord, Selmer, 136 C Cave, Stephen, 117 Cheke, Lucy, 117 Chin, Chuanfei, 3 Crosby, Matthew, 117 D Danziger, Shlomo, 158 Dodig-Crnkovic, Gordana, 19 F Fabra-Boluda, Raül, 175 Ferri, Cèsar, 175 Feyereisl, Jan, 117 Freed, Sam, 187 G Gokmen, Arzu, 248 Govindarajulu, Naveen Sundar, 136 Greif, Hajo, 24 Guazzini, Jodi, 36 H Halina, Marta, 117 Hardalupas, Mahi, 252 Hernández-Orallo, José, 117, 175 Human, Soheil, 40, 190 Hummel, John, 136 K Kane, Thomas B., 255 Keeling, Geoff, 259 L Lewis, Colin WP, 212 Loe, Bao Sheng, 117 Longinotti, David, 43 M Martínez-Plumed, Fernando, 117, 175 Maruyama … 316 Author Index R Ramírez-Quintana, M. José, 175 S Savenkov …

Often people describe the creative act of programming as mysterious (Costa 2015). This paper explores the phenomenology of programming, and examines the following proposal: Programming is a log of actions one would imagine oneself to be doing (in order to achieve a task) after one projects oneself into a world consisting of software mechanisms, such as “the Python environment”. Programming is the formal logging of our imagined actions, in such an imagined world. Our access to our imagination is introspective.

This research study looks at how addiction to Facebook impacts identity. Facebook is the currently the largest social network, boasting over one billion monthly active users. Previous research into the effects Facebook has on an individual has been conducted since the launch of Facebook in 2004, including research on how the use of Facebook impacts on identity, although research is limited. This research study examines the notion of Facebook addiction, and evaluates whether it has an impact on identity. The research was conducted with 32 participants, who were all undergraduate students at a university in the midlands and all between the ages of 18 and 26. The participants were invited to fill out a self-completion questionnaire, asking a variety of questions relating to their use of Facebook and their profiles on the site. Within the questionnaire the Bergen Facebook Addiction Scale (BFAS) was used to determine whether participants were addicted to Facebook using their criterion. The outcomes were that there is a link between Facebook use and the impact it has on identity, however, further research is needed to see whether Facebook as a whole or Facebook addiction cause this impact.

AI has progressed less than other fields of information technology due to a conceptual impasse. Though much effort has been employed to overcome this situation, often it has been from a restricted point-of-view e.g. philosophy alone or algorithms alone. This paper argues for (and exemplifies) an inter-disciplinary tactic for advancing the field of AI that integrates introspection with programming. The paper has two parts: The first outlines an introspective approach that has been largely overlooked and answers some of the (rather heated) arguments that have caused introspection to be sidelined. The second part offers a practical application of this approach - presented as an algorithm.

The main thesis is that introspection is recommended for the development of anthropic AI. Human-like AI, distinct from rational AI, would suit robots for care for the elderly and for other tasks that require interaction with naive humans. “Anthropic AI” is a sub-type of human-like AI, aiming for the pre-cultured, universal intelligence that is available to healthy humans regardless of time and civilisation. This is contrasted with western, modern, well-trained and adult intelligence that is often the focus of AI. Anthropic AI would pick up local cultures and habits, ignoring optimality. Introspection is recommended for the AI developer, as a source of ideas for designing an artificial mind, in the context of technology rather than science. Existing notions of introspection are analysed, and the aspiration for “clean” or “good” introspection is exposed as a mirage. Nonetheless, introspection is shown to be a legitimate source of ideas for AI using considerations of the contexts of discovery vs. justification. Moreover, introspection is shown to be a positively plausible basis for ideas for AI since if a teacher uses introspection to extract mental skills from themselves to transmit them to a student, an AI developer can also use introspection to uncover the human skills that they want to transfer to a computer. Methods and pitfalls of this approach are detailed, including the common error of polluting one's introspection with highly-educated notions such as mathematical methods. Examples are coded and run, showing promising learning behaviour. This is interpreted as a compromise between Classic AI and Dreyfus's tradition. So far AI practitioners have largely ignored the subjective, while the Phenomenologists have not written code – this thesis bridges that gap. One of the examples is shown to have Gadamerian characteristics, as recommended by (Winograd & Flores, 1986). This serves also as a response to Dreyfus's more recent publications critiquing AI (Dreyfus, 2007, 2012).