Journal of Artificial Intelligence and Consciousness

Since artificial intelligence (AI) emerged in the mid-20th century, it has incurred many theoretical criticisms (Dreyfus, H. [1972] What Computers Can’t Do (MIT Press, New York); Dreyfus, H. [1992] What Computers Still Can’t Do (MIT Press, New York); Searle, J. [1980] Minds, brains and programs, Behav. Brain Sci. 3, 417–457; Searle, J. [1984] Minds, Brains and Sciences (Harvard University Press, Cambridge, MA); Searle, J. [1992] The Rediscovery of the Mind (MIT Press, Cambridge, MA); Fodor, J. [2002] The Mind Doesn’t Work that Way: The Scope and Limits of Computational Psychology (MIT Press, Cambridge, MA).). The technical improvements of machine learning and deep learning, though, have been continuing and many breakthroughs have occurred recently. This makes theoretical considerations urgent again: can this new wave of AI fare better than its precursors in emulating or even having human-like minds? I propose a cautious yet positive hypothesis: current AI might create human-like mind, but only if it incorporates certain conceptual rewiring: it needs to shift from a task-based to an agent-based framework, which can be dubbed “Artificial Agential Intelligence” (AAI). It comprises practical reason (McDowell, J. [1979] Virtue and reason, Monist 62(3), 331–350; McDowell, J. [1996] Mind and World (Harvard University Press, Cambridge, MA)), imaginative understanding (Campbell, J. [2020] Causation in Psychology (Harvard University Press, Cambridge, MA)), and animal knowledge (Sosa, E. [2007] A Virtue Epistemology: Apt Belief and Reflective Knowledge, volume 1 (Oxford University Press, Oxford, UK); Sosa, E. [2015] Judgment and Agency (Oxford University Press, Cambridge, MA)). Moreover, I will explore whether and in what way neuroscience-inspired AI and predictive coding (Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. [2017] Neuroscience-inspired artificial intelligence, Neuron 95(2), 245–258) can help carry out this project.

AI Safety researchers attempting to align values of highly capable intelligent systems with those of humanity face a number of challenges including personal value extraction, multi-agent value merger and finally in-silico encoding. State-of-the-art research in value alignment shows difficulties in every stage in this process, but merger of incompatible preferences is a particularly difficult challenge to overcome. In this paper, we assume that the value extraction problem will be solved and propose a possible way to implement an AI solution which optimally aligns with individual preferences of each user. We conclude by analyzing benefits and limitations of the proposed approach.

If qualia are mental, and if the mental is functional, then so are qualia. But, arguably, qualia are not functional. A resolution of this is offered based on a formal similarity between qualia and numbers. Just as certain sets “play the role of” the number 3 in Peano’s axioms, so a certain physical implementation of a color plays the role of, say, red in a (computational) cognitive agent’s “cognitive economy”.

Artificial intelligence (AI) and robotics have made autonomous technical systems a reality that can replace human performance in more and more aspects and tasks, often with better quality and higher speed. This has changed the perspective from the fascination of visionary technology to the question of what will happen to humans as technology gets better and better. Improvement and replacement narratives about the future of humans circulate primarily in trans- and post-humanism, but also engage mass media and church academies. The aim of this paper is to provide an overview and analysis of the recent literature to strengthen and criticize the thesis that in many debates on new technologies and AI across different disciplines (from computer science to philosophy, futurism and the broader public), teleological positions have been taken, according to which the technological perfection constitutes the historical goal of man and his destiny.

In this research, we present a categorical framework to connect research on creativity and cognition for humans and robots, in light of the quantum paradigm. These fields and their relationships suggest a wider vision: modeling human creativity/cognition through quantum computing, and creating robots that can help us learn more about the humans themselves. We represent the human–robot comparison through functors (function generalization). Fundamental elements to understand human creativity are motivation and feedback as aesthetic pleasure. Is it possible to model it? Can the quantum paradigm help us in such an endeavor? We envisage the concept of emergence and quantum computing as decisive keys for creativity. Investigation of robotic models, including swarms of robots, might help shed new light on human creative interactions.

The established theories and frameworks on consciousness in the academic literature as related to artificial intelligence (AI), are rooted in anthropocentricism. Even those theories created intentionally for AI are based on the levels of consciousness as it is understood in humans primarily, and in other animals secondarily. This paper will discuss why such anthropocentric frameworks are built on unsecure foundations. We will do this by comparing the capacities and functions of human and AI cognitive architectures, discussing the ramifications and consequences of the behaviors that stem from these, and looking at the neurological conditions in humans that can give the most promising hints as to what a potential conscious AI entity would look like. The paper ends with a proposed solution for building a nonanthropocentric foundation of cognition that could lead toward a truly AI-focused framework of consciousness.

This paper explores how the development of real-world artificial agents could lead to the evolution of machine consciousnesses with their own moral standards, and give rise to artificial intelligences that subscribe to Robocentrism; an ethical perspective that prioritizes the welfare of artificial agents. In doing so, it explores relevant concepts from contemporary, as well as more traditional literature, and blends theoretical models of consciousness with empirical examples from other scientific domains.

This paper contributes to current literature in several ways, the first of which is by clarifying and solidifying some important prerequisite concepts. At the beginning of the paper, key considerations for developing true artificial intelligence are problematized, and a view on how the emergence of machine consciousness might realistically occur is presented, supported by several empirical examples and thought experiments. Three hierarchical ontological states are categorized and discussed in detail: Sensory experience, instinctual awareness, and reflexive awareness. What this might look like for different forms of artificial agents is also explored.

Second, upon presenting a path to the emergence of machine consciousness, the novel ethical perspective ‘Robocentrism’ is introduced, and a discussion follows on how such a potentiality might come to pass given the pre-existing socio-technological system. For the sake of argument and to move forward with the premise of this paper, an explicit assumption is made, which builds upon the prior section, that the AI in question has reached a state of self-reflexive autonomy such that it could be considered a moral agent, can reason for itself, and learn; that is, code its own improvements. Consequently, the output of this review asserts that as machines become more advanced and capable of autonomous self-learning and improvement, we should reasonably conclude that they will develop their own sense of ethical and moral standards in relation to their surroundings, to engage successfully with wider society and one another.

It is noted that there are many different definitions of and views about qualia, and this makes qualia into a vague concept without much theoretical and constructive value. Here, qualia are redefined in a more general way. It is argued that the redefined qualia will be essential to the mind–body problem, the problem of consciousness and also to the symbol grounding problem, which is inherent in physical symbol systems. Then, it is argued that the redefined qualia are necessary for Artificial Intelligence systems for the operation with meanings. Finally, it is proposed that robots with qualia may be conscious.