Engineering General Intelligence, Part 2 de Ben Goertzel

Engineering General Intelligence, Part 2: The CogPrime Architecture for Integrative, Embodied AGI: Atlantis Thinking Machines, cartea 6

Ben Goertzel

Cassio Pennachin

Nil Geisweiller

The work outlines a detailed blueprint for the creation of an Artificial General Intelligence system with capability at the human level and ultimately beyond, according to the Cog Prime AGI design and the Open Cog software architecture.

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Cuprins

The Open Cog Framework.- Knowledge Representation Using the Atomspace.- Representing Procedural Knowledge.- Emotion, Motivation, Attention and Control.- Attention Allocation.- Economic Goal and Action Selection.- Integrative Procedure Evaluation.- Perceptual and Motor Hierarchies.- Integrating Cog Prime with a Compositional Spatiotemporal Deep Learning Network.- Making DeSTIN Representationally Transparent.- Bridging the Symbolic/Subsymbolic Gap.- Procedure Learning as Program Learning.- Learning Procedures via Imitation, Reinforcement and Correction.- Procedure Learning via Adaptively Biased Hillclimbing.- Probabilistic Evolutionary Procedure Learning.- Probabilistic Logic Networks.- Spatio-Temporal Inference.- Adaptive, Integrative Inference Control.- Pattern Mining..- Speculative Concept Formation.- Dimensional Embedding.- Mental Simulation and Episodic Memory.- Integrative Procedure Learning.- Map Formation.- Communication Between Artificial Minds.- Natural Language Comprehension.- Language Learning via Unsupervised Corpus Analysis.- Natural Language Generation.- Embodied Language Processing.- Natural Language Dialogue.- Summary of Argument for the Cog Prime Approach.

Caracteristici

No other book (or research paper) provides a detailed, carefully-argued design for the creation of Artificial General Intelligence with capability at the human level and beyond No other book (or research paper) bridges the gap between the mathematical and conceptual theory of general intelligence and the practical engineering of real-world AGI software systems with comprehensive human-level capabilities The book gives concrete guidance for the integration of AI subsystems created according to diverse theoretical paradigms such as evolutionary learning, probabilistic reasoning, attractor neural networks and computational linguistics Includes supplementary material: sn.pub/extras