John Carbone – författare

This book explores the subject of artificial psychology and how the field must adapt human neuro-psychological testing techniques to provide adequate cognitive testing of advanced artificial intelligence systems. It shows how classical testing methods will reveal nothing about the cognitive nature of the systems and whether they are learning, reasoning, and evolving correctly; for these systems, the authors outline how testing techniques similar to/adapted from human psychological testing must be adopted, particularly in understanding how the system reacts to failure or relearning something it has learned incorrectly or inferred incorrectly. The authors provide insights into future architectures/capabilities that artificial cognitive systems will possess and how we can evaluate how well they are functioning. It discusses at length the notion of human/AI communication and collaboration and explores such topics as knowledge development, knowledge modeling and ambiguity management, artificial cognition and self-evolution of learning, artificial brain components and cognitive architecture, and artificial psychological modeling.Explores the concepts of Artificial Psychology and Artificial Neuroscience as applied to advanced artificially cognitive systems;Provides insight into the world of cognitive architectures and biologically-based computing designs which will mimic human brain functionality in artificial intelligent systems of the future;Provides description and design of artificial psychological modeling to provide insight into how advanced artificial intelligent systems are learning and evolving;Explores artificial reasoning and inference architectures and the types of modeling and testing that will be required to "trust" an autonomous artificial intelligent systems.

This book explores the subject of artificial psychology and how the field must adapt human neuro-psychological testing techniques to provide adequate cognitive testing of advanced artificial intelligence systems. It shows how classical testing methods will reveal nothing about the cognitive nature of the systems and whether they are learning, reasoning, and evolving correctly; for these systems, the authors outline how testing techniques similar to/adapted from human psychological testing must be adopted, particularly in understanding how the system reacts to failure or relearning something it has learned incorrectly or inferred incorrectly. The authors provide insights into future architectures/capabilities that artificial cognitive systems will possess and how we can evaluate how well they are functioning. It discusses at length the notion of human/AI communication and collaboration and explores such topics as knowledge development, knowledge modeling and ambiguity management, artificial cognition and self-evolution of learning, artificial brain components and cognitive architecture, and artificial psychological modeling.Explores the concepts of Artificial Psychology and Artificial Neuroscience as applied to advanced artificially cognitive systems;Provides insight into the world of cognitive architectures and biologically-based computing designs which will mimic human brain functionality in artificial intelligent systems of the future;Provides description and design of artificial psychological modeling to provide insight into how advanced artificial intelligent systems are learning and evolving;Explores artificial reasoning and inference architectures and the types of modeling and testing that will be required to "trust" an autonomous artificial intelligent systems.

This book presents the proceedings of the 25th Society for Design and Process Science Conference (SDPS 2024), which took place in Bologna, Italy on October 6-9, 2024. The 2024 conference centers on generative artificial intelligence (GenAI) and its impact on academia and industry. The papers focus on the generational challenges that GenAI presents, which industry leaders agree constitutes a sea change in technology not seen since the Industrial Revolution. The conference emphases that, as the AI/GenAI community creates vast new and novel opportunities at a rapid pace, it consequently creates challenges with lack of understanding and immature designs, processes, research, engineering, while also exposing gaps in formal AI/GenAI design and process education. The authors also discuss, for the sake of analysis, the general fear of unknown AI/GenAI technologies, as well as system ambiguity and disguised complexities. The authors aim to present solutions that help to mitigate risk in these in critical environments (e.g., medical, financial, cyber defense etc). To better serve developers and modern business leaders with guidance to innovate, mitigate risk, and capitalize on novel advancements in AI/GenAI, the Society for Design and Process Science (SDPS) brings together the advanced academic research community with industry to more qualitatively harness the power of formal AI/GenAI design and process to accelerate science and business.

This book presents the proceedings of the 25th Society for Design and Process Science Conference (SDPS 2024), which took place in Bologna, Italy on October 6-9, 2024. The 2024 conference centers on generative artificial intelligence (GenAI) and its impact on academia and industry. The papers focus on the generational challenges that GenAI presents, which industry leaders agree constitutes a sea change in technology not seen since the Industrial Revolution. The conference emphases that, as the AI/GenAI community creates vast new and novel opportunities at a rapid pace, it consequently creates challenges with lack of understanding and immature designs, processes, research, engineering, while also exposing gaps in formal AI/GenAI design and process education. The authors also discuss, for the sake of analysis, the general fear of unknown AI/GenAI technologies, as well as system ambiguity and disguised complexities. The authors aim to present solutions that help to mitigate risk in these in critical environments (e.g., medical, financial, cyber defense etc). To better serve developers and modern business leaders with guidance to innovate, mitigate risk, and capitalize on novel advancements in AI/GenAI, the Society for Design and Process Science (SDPS) brings together the advanced academic research community with industry to more qualitatively harness the power of formal AI/GenAI design and process to accelerate science and business.