scholarly journals Nonconscious Cognitive Suffering: Considering Suffering Risks of Embodied Artificial Intelligence

Philosophies ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 24
Author(s):  
Steven Umbrello ◽  
Stefan Lorenz Sorgner
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Embodied Cognition ◽  
Information Integration ◽  
Physical State ◽  
Conscious State ◽  
Human Cognition ◽  
Machine Consciousness

Strong arguments have been formulated that the computational limits of disembodied artificial intelligence (AI) will, sooner or later, be a problem that needs to be addressed. Similarly, convincing cases for how embodied forms of AI can exceed these limits makes for worthwhile research avenues. This paper discusses how embodied cognition brings with it other forms of information integration and decision-making consequences that typically involve discussions of machine cognition and similarly, machine consciousness. N. Katherine Hayles’s novel conception of nonconscious cognition in her analysis of the human cognition-consciousness connection is discussed in relation to how nonconscious cognition can be envisioned and exacerbated in embodied AI. Similarly, this paper offers a way of understanding the concept of suffering in a way that is different than the conventional sense of attributing it to either a purely physical state or a conscious state, instead of grounding at least a type of suffering in this form of cognition.

scholarly journals Quantum Computing and Cognitive Simulation

2021 ◽  
Author(s):  
Martha Lewis
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Quantum Computing ◽  
Quantum Theory ◽  
Human Cognition ◽  
Cognitive Simulation ◽  
Concept Combination

In this chapter we look at three main areas in which quantum theory has beenapplied to understanding human cognition: the modelling of similarityjudgements; the modelling of decision making; and the modelling ofcategorisation and concept combination. We summarize the cognitive phenomenathat have been described by quantum theory. We describe how quantum theory hasbeen used to address each of these phenomena, as well as discussingquantum-theoretic approaches to modelling neuronal-level phenomena. Finally, wediscuss the implications for using quantum computing to model cognitivephenomena and artificial intelligence more generally.

The Role of Theories of Embodied Cognition in Research and Modeling of Emotions

2019 ◽  
Vol 62 (5) ◽  
pp. 124-138
Author(s):  
Alexandra V. Shiller
Keyword(s):  
Artificial Intelligence ◽  
Embodied Cognition ◽  
Mirror Neurons ◽  
Empirical Studies ◽  
Unknown Quantity ◽  
Emotional Processes ◽  
Strong Artificial Intelligence ◽  
European Philosophy ◽  
Postmodern Philosophy

The article analyzes the role of theories of embodied cognition for the development of emotion research. The role and position of emotions changed as philosophy developed. In classical and modern European philosophy, the idea of the “primacy of reason” prevailed over emotions and physicality, emotions and affective life were described as low-ranking phenomena regarding cognitive processes or were completely eliminated as an unknown quantity. In postmodern philosophy, attention focuses on physicality and sensuality, which are rated higher than rational principle, mind and intelligence. Within the framework of this approach, there is a recently emerged theory of embodied cognition, which allows to take a fresh look at the place of emotions in the architecture of mental processes – thinking, perception, memory, imagination, speech. The article describes and analyzes a number of empirical studies showing the impossibility of excluding emotional processes and the significance of their research for understanding the architecture of embodied cognition. However, the features of the architecture of embodied cognition remain unclear, and some of the discoveries of recent years (mirror neurons or neurons of simulation) rather raise new questions and require further research. The rigorously described and clear architecture of the embodied cognition can grow the theoretical basis that will allow to advance the studies of learning processes, language understanding, psychotherapy techniques, social attitudes and stereotypes, highlight the riddle of consciousness and create new theories of consciousness or even create an anthropomorphic artificial intelligence that is close to “strong artificial intelligence.”

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

Embodying algorithms, enactive artificial intelligence and the extended cognition: You can see as much as you know about algorithm

2021 ◽  
pp. 016555152098549
Author(s):  
Donghee Shin
Keyword(s):  
Artificial Intelligence ◽  
Embodied Cognition ◽  
Cognitive Processes ◽  
Process Model ◽  
Extended Cognition ◽  
Dual Process ◽  
Personalized Recommendation ◽  
Normative Values ◽  
And Performance ◽  
Concise Guide

The recent proliferation of artificial intelligence (AI) gives rise to questions on how users interact with AI services and how algorithms embody the values of users. Despite the surging popularity of AI, how users evaluate algorithms, how people perceive algorithmic decisions, and how they relate to algorithmic functions remain largely unexplored. Invoking the idea of embodied cognition, we characterize core constructs of algorithms that drive the value of embodiment and conceptualizes these factors in reference to trust by examining how they influence the user experience of personalized recommendation algorithms. The findings elucidate the embodied cognitive processes involved in reasoning algorithmic characteristics – fairness, accountability, transparency, and explainability – with regard to their fundamental linkages with trust and ensuing behaviors. Users use a dual-process model, whereby a sense of trust built on a combination of normative values and performance-related qualities of algorithms. Embodied algorithmic characteristics are significantly linked to trust and performance expectancy. Heuristic and systematic processes through embodied cognition provide a concise guide to its conceptualization of AI experiences and interaction. The identified user cognitive processes provide information on a user’s cognitive functioning and patterns of behavior as well as a basis for subsequent metacognitive processes.

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