Scientific requirements for an engineered model of consciousness

The building of a non-natural conscious system requires more than the design of physical or virtual machines with intuitively conceived abilities, philosophically elucidated architecture or hardware homologous to an animal’s brain. Human society might one day treat a type of robot or computing system as an artificial person. Yet that would not answer scientific questions about the machine’s consciousness or otherwise. Indeed, empirical tests for consciousness are impossible because no such entity is denoted within the theoretical structure of the science of mind, i.e. psychology. However, contemporary experimental psychology can identify if a specific mental process is conscious in particular circumstances, by theory-based interpretation of the overt performance of human beings. Thus, if we are to build a conscious machine, the artificial systems must be used as a test-bed for theory developed from the existing science that distinguishes conscious from non-conscious causation in natural systems. Only such a rich and realistic account of hypothetical processes accounting for observed input/output relationships can establish whether or not an engineered system is a model of consciousness. It follows that any research project on machine consciousness needs a programme of psychological experiments on the demonstration systems and that the programme should be designed to deliver a fully detailed scientific theory of the type of artificial mind being developed – a Psychology of that Machine

An Integration of Deep Learning and Neuroscience for Machine Consciousness

Conscious processing is a useful aspect of brain function that can be used as a model to design artificial-intelligence devices There are still certain computational features that our conscious brains possess and which machines currently fail to perform those This paper discusses the necessary elements needed to make the device conscious and suggests if those implemented the resulting machine would likely to be considered conscious Consciousness mainly presented as a computational tool that evolved to connect the modular organization of the brain Specialized modules of the brain process information unconsciously and what we subjectively experience as consciousness is the global availability of data which is made possible by a non modular global workspace During conscious perception the global neuronal work space at parieto-frontal part of the brain selectively amplifies relevant pieces of information Supported by large neurons with long axons which makes the long-distance connectivity possible the selected portions of information stabilized and transmitted to all other brain modules The brain areas that have structuring ability seem to match to a specific computational problem The global workspace maintains this information in an active state for as long as it is needed In this paper a broad range of theories and specific problems have been discussed which need to be solved to make the machine conscious Later particular implications of these hypotheses for research approach in neuroscience and machine learning are debate

Machines and morality:: juridical and philosophical considerations

This article studies the possibilities of giving morality to machines and autonomous systems. Its hypothesis is that the design strategies for the development of machines that make moral judgments should take into account a vast complex of contingencies, which are related to each context in which they are implemented — being its user/recipient, its developer, and the purposes for which its use is intended, the most important ones. As a result, it is clear that machines, currently, are not self-conscious yet, but a posture influenced by ethical behaviorism and hybrid design, combining pre-programmed moral postulates and machine learning for the contextualization of each machine, can contribute with possibilities for giving them moral status. Methodology: hypothetical-deductive procedure method, with a qualitative approach and bibliographic review research techniqueThis article studies the possibilities of giving morality to machines and autonomous systems. Its hypothesis is that the design strategies for the development of machines that make moral judgments should take into account a vast complex of contingencies, which are related to each context in which they are implemented — being its user/recipient, its developer, and the purposes for which its use is intended, the most important ones. As a result, it is clear that machines, currently, are not self-conscious yet, but a posture influenced by ethical behaviorism and hybrid design, combining pre-programmed moral postulates and machine learning for the contextualization of each machine, can contribute with possibilities for giving them moral status. Methodology: hypothetical-deductive procedure method, with a qualitative approach and bibliographic review research technique

Racial categories in machine learning

Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexity challenges the paradigm of considering fairness to be a formal property of supervised learning with respect to protected personal attributes. Racial identity is not simply a personal subjective quality. For people labeled "Black" it is an ascribed political category that has consequences for social differentiation embedded in systemic patterns of social inequality achieved through both social and spatial segregation. In the United States, racial classification can best be understood as a system of inherently unequal status categories that places whites as the most privileged category while signifying the Negro/black category as stigmatized. Social stigma is reinforced through the unequal distribution of societal rewards and goods along racial lines that is reinforced by state, corporate, and civic institutions and practices. This creates a dilemma for society and designers: be blind to racial group disparities and thereby reify racialized social inequality by no longer measuring systemic inequality, or be conscious of racial categories in a way that itself reifies race. We propose a third option. By preceding group fairness interventions with unsupervised learning to dynamically detect patterns of segregation, machine learning systems can mitigate the root cause of social disparities, social segregation and stratification, without further anchoring status categories of disadvantage

GR-406 Federated Learning in Cardiac Diagnostics: Balancing Predictive Accuracy with Data Privacy in Heart Sound Classification

Cardiovascular diseases account for nearly a third of global deaths, posing a challenge that machine learning can help address. However, data privacy concerns hinder the direct application of conventional machine learning in this sensitive area. This paper explores Federated Learning (FL) as a decentralized strategy to mitigate these concerns by allowing for local data processing. FL\u27s design ensures that only processed updates, not raw data, are shared with a central server, maintaining individual privacy. Our research assesses FL\u27s practicality and effectiveness in predicting heart disease while adhering to ethical and legal norms. We build upon previous studies, such as Wanyong et al.\u27s work on heart sound analysis with FL, to underline its privacy-preserving benefits. This study aims to improve healthcare outcomes with machine learning while setting a privacy-conscious benchmark for future research

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries

Mathematical modeling of lithium-ion batteries (LiBs) is a primary challenge in advanced battery management. This paper proposes two new frameworks to integrate physics-based models with machine learning to achieve high-precision modeling for LiBs. The frameworks are characterized by informing the machine learning model of the state information of the physical model, enabling a deep integration between physics and machine learning. Based on the frameworks, a series of hybrid models are constructed, through combining an electrochemical model and an equivalent circuit model, respectively, with a feedforward neural network. The hybrid models are relatively parsimonious in structure and can provide considerable voltage predictive accuracy under a broad range of C-rates, as shown by extensive simulations and experiments. The study further expands to conduct aging-aware hybrid modeling, leading to the design of a hybrid model conscious of the state-of-health to make prediction. The experiments show that the model has high voltage predictive accuracy throughout a LiB's cycle life.Comment: 15 pages, 10 figures, 2 tables. arXiv admin note: text overlap with arXiv:2103.1158

An Integration of Deep Learning and Neuroscience for Machine Consciousness

Conscious processing is a useful aspect of brain function that can be used as a model to design artificial-intelligence devices. There are still certain computational features that our conscious brains possess, and which machines currently fail to perform those. This paper discusses the necessary elements needed to make the device conscious and suggests if those implemented, the resulting machine would likely to be considered conscious. Consciousness mainly presented as a computational tool that evolved to connect the modular organization of the brain. Specialized modules of the brain process information unconsciously and what we subjectively experience as consciousness is the global availability of data, which is made possible by a non modular global workspace. During conscious perception, the global neuronal work space at parieto-frontal part of the brain selectively amplifies relevant pieces of information. Supported by large neurons with long axons, which makes the long-distance connectivity possible, the selected portions of information stabilized and transmitted to all other brain modules. The brain areas that have structuring ability seem to match to a specific computational problem. The global workspace maintains this information in an active state for as long as it is needed. In this paper, a broad range of theories and specific problems have been discussed, which need to be solved to make the machine conscious. Later particular implications of these hypotheses for research approach in neuroscience and machine learning are debated