Cognitive Computing and Cognitive Models

Flashcards summarizing key concepts related to cognitive computing and cognitive models.

Cognitive Computing

A branch of artificial intelligence (AI) that aims to simulate human thought processes in a computerized model.

Key Features of Cognitive Computing

Includes machine learning, natural language processing, speech recognition, image processing, and neural networks.

IBM Watson

A powerful cognitive computing system developed by IBM designed to understand, reason, learn, and interact using natural language.

Natural Language Processing (NLP)

The ability of a computer system to understand and interpret human language, both spoken and written.

Machine Learning in IBM Watson

The continuous learning from new data for improved accuracy and relevance.

Declarative Cognitive Models

Models that provide answers with supporting evidence and confidence levels, representing 'what' a person knows.

Logic-Based Cognitive Models

Models that use formal logic systems to replicate human reasoning, focusing on how conclusions are drawn from known premises.

Bayesian Models of Cognition

Models that use probabilistic reasoning to update beliefs based on uncertain or incomplete information.

Connectionist Models of Cognition

Models that simulate cognition using networks of interconnected units, similar to neurons, focusing on pattern recognition and learning.

Augmented Intelligence

The use of technology to enhance human intelligence, supporting decision-making and reasoning rather than replacing human thought.

Adaptive Learning

The capability of cognitive systems to learn and adapt from data patterns and feedback.

Contextual Understanding

The ability of cognitive systems to understand meaning based on context such as time, location, and domain.