Artificial consciousness and the consciousness-attention dissociation

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness

The Emperor\u27s Clothes

Argues that neoclassical economics is primarily a theory of the human individual in economic life but contemporary mainstream economics does not posses a theory of the human individual. Definition of mainstream economics; Origin of the Enlightenment theory of the individual that came to underlie neoclassical economics at the end of the nineteenth century; Problems in neoclassical economics\u27 treatment of individuals

Motivations, Values and Emotions: 3 sides of the same coin

This position paper speaks to the interrelationships between the three concepts of motivations, values, and emotion. Motivations prime actions, values serve to choose between motivations, emotions provide a common currency for values, and emotions implement motivations. While conceptually distinct, the three are so pragmatically intertwined as to differ primarily from our taking different points of view. To make these points more transparent, we briefly describe the three in the context a cognitive architecture, the LIDA model, for software agents and robots that models human cognition, including a developmental period. We also compare the LIDA model with other models of cognition, some involving learning and emotions. Finally, we conclude that artificial emotions will prove most valuable as implementers of motivations in situations requiring learning and development

The Mental Database

This article uses database, evolution and physics considerations to suggest how the mind stores and processes its data. Its innovations in its approach lie in:- A) The comparison between the capabilities of the mind to those of a modern relational database while conserving phenomenality. The strong functional similarity of the two systems leads to the conclusion that the mind may be profitably described as being a mental database. The need for material/mental bridging and addressing indexes is discussed. B) The consideration of what neural correlates of consciousness (NCC) between sensorimotor data and instrumented observation one can hope to obtain using current biophysics. It is deduced that what is seen using the various brain scanning methods reflects only that part of current activity transactions (e.g. visualizing) which update and interrogate the mind, but not the contents of the integrated mental database which constitutes the mind itself. This approach yields reasons why there is much neural activity in an area to which a conscious function is ascribed (e.g. the amygdala is associated with fear), yet there is no visible part of its activity which can be clearly identified as phenomenal. The concept is then situated in a Penrosian expanded physical environment, requiring evolutionary continuity, modularity and phenomenality.Several novel Darwinian advantages arising from the approach are described

From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)

This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness

Cognitive control: componential or emergent?

The past twenty-five years have witnessed an increasing awareness of the importance of cognitive control in the regulation of complex behavior. It now sits alongside attention, memory, language and thinking as a distinct domain within cognitive psychology. At the same time it permeates each of these sibling domains. This paper reviews recent work on cognitive control in an attempt to provide a context for the fundamental question addressed within this Topic: is cognitive control to be understood as resulting from the interaction of multiple distinct control processes or are the phenomena of cognitive control emergent

Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007 joint invited lectur