Is a Thinking Machine Possible

The meaning of AI has undergone drastic changes during the last 60 years of AI discourse(s). What we talk about when saying “AI” is not what it meant in 1958, when John McCarthy, Marvin Minsky and their colleagues started using the term. Take game design as an example: When the Unreal game engine introduced "AI" in 1999, they were mainly talking about pathfinding. For Epic Megagames, the producers of Unreal, an AI was just a bot or monster whose pathfinding capabilities had been programmed in a few lines of code to escape an enemy. This is not "intelligence" in the Minskyan understanding of the word (and even less what Alan Turing had in mind when he designed the Turing test). There are also attempts to differentiate between AI, classical AI and "Computational Intelligence" (Al-Jobouri 2017). The latter is labelled CI and is used to describe processes such as player affective modelling, co-evolution, automatically generated procedural environments, etc. Artificial intelligence research has been commonly conceptualised as an attempt to reduce the complexity of human thinking. (cf. Varela 1988: 359-75) The idea was to map the human brain onto a machine for symbol manipulation – the computer. (Minsky 1952; Simon 1996; Hayles 1999) Already in the early days of what we now call “AI research” McCulloch and Pitts commented on human intelligence and proposed in 1943 that the networking of neurons could be used for pattern recognition purposes (McCulloch/Pitts 1943). Trying to implement cerebral processes on digital computers was the method of choice for the pioneers of artificial intelligence research. The “New AI” is no longer concerned with the needs to observe the congruencies or limitations of being compatible with the biological nature of human intelligence: “Old AI crucially depended on the functionalist assumption that intelligent systems, brains or computers, carry out some Turing-equivalent serial symbol processing, and that the symbols processed are a representation of the field of action of that system.” (Pickering 1993, 126) The ecological approach of the New AI has its greatest impact by showing how it is possible “to learn to recognize objects and events without having any formal representation of them stored within the system.” (ibid, 127) The New Artificial Intelligence movement has abandoned the cognitivist perspective and now instead relies on the premise that intelligent behaviour should be analysed using synthetically produced equipment and control architectures (cf. Munakata 2008). Kate Crawford (Microsoft Research) has recently warned against the impact that current AI research might have, in a noteworthy lecture titled: AI and the Rise of Fascism. Crawford analysed the risks and potential of AI research and asked for a critical approach in regard to new forms of data-driven governmentality: “Just as we are reaching a crucial inflection point in the deployment of AI into everyday life, we are seeing the rise of white nationalism and right-wing authoritarianism in Europe, the US and beyond. How do we protect our communities – and particularly already vulnerable and marginalized groups – from the potential uses of these systems for surveillance, harassment, detainment or deportation?” (Crawford 2017) Following Crawford’s critical assessment, this issue of the Digital Culture & Society journal deals with the impact of AI in knowledge areas such as computational technology, social sciences, philosophy, game studies and the humanities in general. Subdisciplines of traditional computer sciences, in particular Artificial Intelligence, Neuroinformatics, Evolutionary Computation, Robotics and Computer Vision once more gain attention. Biological information processing is firmly embedded in commercial applications like the intelligent personal Google Assistant, Facebook’s facial recognition algorithm, Deep Face, Amazon’s device Alexa or Apple’s software feature Siri (a speech interpretation and recognition interface) to mention just a few. In 2016 Google, Facebook, Amazon, IBM and Microsoft founded what they call a Partnership on AI. (Hern 2016) This indicates a move from academic research institutions to company research clusters. We are in this context interested in receiving contributions on the aspects of the history of institutional and private research in AI. We would like to invite articles that observe the history of the notion of “artificial intelligence” and articles that point out how specific academic and commercial fields (e.g. game design, aviation industry, transport industry etc.) interpret and use the notion of AI. Against this background, the special issue Rethinking AI will explore and reflect the hype of neuroinformatics in AI discourses and the potential and limits of critique in the age of computational intelligence. (Johnston 2008; Hayles 2014, 199-210) We are inviting contributions that deal with the history, theory and the aesthetics of contemporary neuroscience and the recent trends of artificial intelligence. (cf. Halpern 2014, 62ff) Digital societies increasingly depend on smart learning environments that are technologically inscribed. We ask for the role and value of open processes in learning environments and we welcome contributions that acknowledge the regime of production as promoted by recent developments in AI. We particularly welcome contributions that are historical and comparative or critically reflective about the biological impact on social processes, individual behaviour and technical infrastructure in a post-digital and post-human environment? What are the social, cultural and ethical issues, when artificial neuronal networks take hold in digital cultures? What is the impact on digital culture and society, when multi-agent systems are equipped with license to act?

Artificial Intelligence (AI) and the latest advancements in Information Technology (IT) have drastically altered our view of the world. With the ability to process and analyze vast amounts of data in real-time, AI has revolutionized several industries, including healthcare, finance, and manufacturing. For instance, AI-powered medical devices can assist doctors in making diagnoses and predicting patient outcomes more accurately, while also helping them identify early warning signs of diseases. Furthermore, the rise of Machine Learning (ML) has enabled organizations to leverage predictive analytics to gain valuable insights into consumer behavior and market trends. For example, online retailers such as Amazon use AI algorithms to personalize product recommendations based on a customer's browsing and purchase history. In this way, AI-powered systems are able to offer customized experiences and improve overall customer satisfaction. A key challenge on that is the potential impact of AI on human consciousness and perception. As AI systems become more advanced and more integrated into our daily lives, there is a risk that they may alter our perception of the world and our understanding of reality. This raises important questions about the ethical implications of AI and the need to ensure that AI systems are designed and used in a responsible and ethical manner. A shift in a new revolutionary science such as AI and applications such as chatGPT is telling us again that man is not at the center of the universe as an intelligent being. The ideal model of the "Cartesian Man" embodied the basic tenets of Renaissance humanism, which considered man the centre of the universe, limitless in his capacities for development, led to the notion that men should try to embrace all knowledge and develop their own capacities as fully as possible, unfortunately it seems that AI is replacing those limitless capacities! My question is whether AI as a potentially transformative technology that has the potential to enhance our understanding of the world and ourselves.

IEEE Access

The goal of the paper is to find means for the unification of human-machine duality in collective behavior of people and machines, by conciliating approaches that proceed in opposite directions. The first approach proceeds top-down from non-formalizable, cognitive, uncaused, and chaotic human consciousness towards purposeful and sustainable human-machine interaction. The second approach proceeds bottom-up from intelligent machines towards high-end computing and is based on formalizable models leveraging multi-agent architectures. The resulting work reviews the extent, the merging points, and the potential of hybrid artificial intelligence frameworks that accept idea of strong artificial intelligence. These models concern the pairing of connectionist and cognitive architectures, conscious and unconscious actions, symbolic and conceptual realizations, emergent and brain-based computing, automata and subjects. The special authors' convergent methodology is considered, which is based on the integration of inverse problem-solving on topological spaces, cognitive modelling, quantum field theory, category theory methods, and holonic approaches. It aims to a more purposeful and sustainable human-machine interaction in form of algorithms or requirements, rules of strategic conversations or network brainstorming, and cognitive semantics. The paper addresses reducing the impact of AI development on breaking ethics. The findings are used to provide perspectives on the shaping of societal, ethical, and normative aspects in the symbiosis between humans and machines. Implementations in real practice are represented. INDEX TERMS cognitive semantics, category theory, human-machine duality, hybrid artificial intelligence, holonic systems, stability in dynamical systems.

The present article addresses the question of coexistence of non-human machines and the process of thinking. By drawing rationale and cues from earlier research efforts, a new scheme of thought for storage of learned knowledge – as an upper envelope – followed by subsequent reconstruction of brain's neural network, is proposed. This designed concept is then put into perspective by providing arguments from the existing research, thereby making a small effort towards realizing machines that may be conscious.

scip Labs, 2018

Cognition comprises mental process of the human brain. Artificial Intelligence tries to mimic these processes to solve complex problems and handle massive amounts of data. Serial vs. parallel and controlled vs. automated processes are the basis of cognitive sciences. Logic, probability theory and heuristics represent the pillars of theory formation for the human-machine comparison. Artificial neural networks (mimicking the human brain) are popular, because great advancements in specific fields were achieved. Systemic problem solving is still visionary, but a number of research projects are promising. Reciprocal, positive influences across disciplines might lead to rapid transformation. Polemics, as well idealism are out of place. Analysis must adhere to scientific and ethical standards with long-term orientation for the public good. After the physiological review, the next chapter will focus on consciousness (as a part of what gives life to physiology).