Specific AI, also known as Narrow AI, focuses on excelling in a specific task or domain, contrasting with the broader capabilities of AGI.
Artificial Intelligence is the field concerned with creating machines that can perform tasks requiring intelligence when performed by humans.
Cognitive Science combines AI models and psychological experiments to construct testable theories of human cognition.
The Turing Test, also known as the Imitation Game, is a test proposed by Alan Turing to determine if a machine can exhibit intelligent behavior indistinguishable from that of a human.
Natural Language Processing (NLP) involves using AI to comprehend and interact with human language, enabling technologies like voice assistants and sentiment analysis.
The rational agent approach focuses on designing agents that act to achieve the best outcome, or the best expected outcome under uncertainty.
AI algorithms may inadvertently learn and perpetuate biases present in the training data, leading to biased decision-making if not addressed.
The quality and preparation of data are crucial for AI algorithms to learn accurately and produce reliable results.
AGI, also known as General AI, refers to AI that possesses expertise in numerous domains, although it is not yet practically viable.
Sentiment analysis assesses the emotional sentiment expressed in written content, aiding applications like customer support and marketing.
An agent is an entity capable of perceiving its environment and taking actions to achieve goals.
Artificial Intelligence (AI) sparks curiosity and imagination due to its potential to revolutionize various aspects of technology and human life.
Machine learning is a key technique utilized by Narrow AI to improve its capabilities by learning from data examples.
Deep learning is the subset of machine learning that draws inspiration from the complex interconnections among neurons in the human brain. It involves the creation of artificial neural networks consisting of multiple layers of mathematical probability. These networks are designed to mimic the way our brain processes information, enabling tasks such as speech recognition and language understanding.
AI's foundation draws from various disciplines such as mathematics, computer engineering, and neuroscience to build the necessary knowledge and tools for AI development.
Traditional programs follow predefined rules, while machine learning learns patterns and behaviors from actual data.
