Journal of Artificial Intelligence and Consciousness

In this paper, we present a unifying framework to study consciousness based on algorithmic information theory (AIT). We take as a premise that “there is experience” and focus on the requirements for structured experience (${𝒮}$) — the spatial, temporal, and conceptual organization of our first-person experience of the world and of ourselves as agents in it. Our starting point is the insight that access to good models — succinct and accurate generative programs of world data — is crucial for homeostasis and survival. We hypothesize that the successful comparison of such models with data provides the structure to experience. Building on the concept of Kolmogorov complexity, we can associate the qualitative aspects of ${𝒮}$ with the algorithmic features of the model, including its length, which reflects the structure discovered in the data. Moreover, a modeling system tracking structured data will display dimensionality reduction and criticality features that can be used empirically to quantify the structure of the program run by the agent. KT provides a consistent framework to define the concepts of life and agent and allows for the comparison between artificial agents and ${𝒮}$-reporting humans to provide an educated guess about agent experience. A first challenge is to show that a human agent has ${𝒮}$ to the extent they run encompassing and compressive models tracking world data. For this, we propose to study the relation between the structure of neurophenomenological, physiological, and behavioral data. The second is to endow artificial agents with the means to discover good models and study their internal states and behavior. We relate the algorithmic framework to other theories of consciousness and discuss some of its epistemological, philosophical, and ethical aspects.

If one had a full understanding of consciousness then it should be possible to create artificial consciousness. It is frequently argued that qualia are subjective and are impossible to re-create artificially. However, qualia are grounded in reality and therefore are not arbitrary. We show this for the quale color. The perceived color corresponds to a three-dimensional value that describes the spectral reflectance function of an object. Perceived color is comparable across individuals. Given this result for color, it is presumed that this also holds for other qualia like pain, hunger or love. According to the theory presented here, an assembly of neurons processes perceptions and is in charge of communicating this information to other members of the peer group. This assembly corresponds to conscious information processing. The job of this assembly is (a) to analyze what the body experiences (internal and external), (b) to keep a record of it, and (c) to explain these experiences to members of the peer group.

This paper considers several aspects of the relationship between size, structure, speed of propagation and the number of autonomous cognitive agents in a neural network. While memory and function generation capacities of neural networks with scale-invariant structure have been investigated extensively, the number of autonomous agents, associated with the simultaneous processing of the independent components of the data, has not received prior attention. We propose the emergence of the dichotomy of causal and noncausal regions created by the speed of propagation, in which the autonomous cognitive agents are not bound in a causal relationship with other agents. Arguments are presented for why the count of autonomous agents is best estimated with respect to the dimensionality of the underlying space. The number of autonomous agents obtained for the human brain equals 25, and it is significant that the number in the sub-system modules also turns out to be close to the same value. This number equals the number of enumerative categories of reality in at least one philosophical tradition, and this coincidence may be attributed to the capacity of consciousness to reflect on its own structure. It is possible that near equality across layers provides a special uniqueness to the human brain. We propose that the findings of this study will be useful in the design of neural-network-based AI systems that are designed to emulate human cognitive capacity.

[, ] offered a novel Gödelian proposal which purported to reconcile the apparently contradictory theses that (1) we can talk, in a non-trivial way, of mental causation being a real phenomenon and that (2) mental activity is ultimately grounded in low-level rule-governed neural processes. In this paper, we critically investigate Hofstadter’s analogical appeals to [] First Incompleteness Theorem, whose “diagonal” proof supposedly contains the key ideas required for understanding both consciousness and mental causation. We maintain that bringing sophisticated results from Mathematical Logic into play cannot furnish insights which would otherwise be unavailable. Lastly, we conclude that there are simply too many weighty details left unfilled in Hofstadter’s proposal. These really need to be fleshed out before we can even hope to say that our understanding of classical mind-body problems has been advanced through metamathematical parallels with Gödel’s work.

This paper discusses the collective intelligent behavior of wholes made out of numerous individuals, otherwise known as Swarm Intelligence (SI). By and large, SI is defined as a kind of a problem-solving ability that emerges when a large number of relatively simple information-processing units, whether animate or artificial, interact locally with each other and with the environment. In this paper, we examine whether SI systems can be thought of as intelligent, according to an accepted definition of Universal Intelligence, whether the emergence of such collective “mind behavior” can be deduced from the simple rules that govern the behavior of the individuals of the swarm, and whether we can reach, through analogical reasoning, new insights regarding physicalist–reductionist views about human intelligence.