Crowder / Carbone / Friess Artificial Psychology
1. Auflage 2019
ISBN: 978-3-030-17081-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Psychological Modeling and Testing of AI Systems
E-Book, Englisch, 169 Seiten
Reihe: Intelligent Technologies and Robotics (R0)
ISBN: 978-3-030-17081-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
- 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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Weitere Infos & Material
Chapter 1. Introduction: Psychology and Technology.- Chapter 2. Systems-Level Thinking for Artificial Intelligent Systems.- Chapter 3. Psychological Constructs for AI Systems: The Information Continuum.- Chapter 4. Human-AI Collaboration.- Chapter 5. Abductive Artificial Intelligence Learning Models.- Chapter 6. Artificial Creativity and Self-Evolution: Abductive Reasoning in Artificial Life Forms.- Chapter 7. Artificial Intelligent Inferences utilizing Occam Abduction.- Chapter 8. Artificial Neural Diagnostics and Prognostics: Self-Soothing in Cognitive Systems.- Chapter 9. Ontology-Based Knowledge Management for Artificial Intelligent Systems.- Chapter 10. Cognitive Control of Self-Evolving Life Forms (SELF) utilizing Artificial Procedural Memories.- Chapter 11. Methodologies for Continuous, Life-Long Machine Learning for AI Systems.- Chapter 12. Implicit Learning in Artificial Intelligence.- Chapter 13. Data Analytics: The Big Data Analytics Process (BDAP) Architecture.- Chapter 14.Conclusions and Next Steps.




