Knowra Artificial intelligence Artificial intelligence Artificial intelligence is the design and study of machines that perform tasks associated with intelligent behavior, such as learning, reasoning, perception, and language use.
Machine learning : A field of artificial intelligence in which systems improve at tasks by learning patterns from data or experience. Learning from examples is the dominant route to building many current AI systems.
Algorithm : A finite, precisely specified procedure for solving a problem or carrying out a computation. AI methods are algorithms implemented to transform inputs into decisions or outputs.
Alan Turing : A British mathematician and computer scientist whose work helped establish theoretical computer science. His 1950 essay proposed an influential behavioral test for machine intelligence.
Computer vision : The field of enabling computers to interpret and derive information from images and video. Vision systems use AI to identify objects, scenes, and patterns in visual data.
Algorithmic bias : Systematic and unfair outcomes produced or amplified by algorithmic systems. AI can reproduce disparities present in its data, objectives, or deployment.
Artificial neural network : A computing model of interconnected units that transforms inputs through weighted connections. Neural networks power many AI systems for perception, language, and prediction.
Data : Recorded observations or measurements that can be stored, processed, and analyzed. Training data supplies examples from which many AI models estimate patterns.
Dartmouth workshop : A 1956 summer research project that helped establish artificial intelligence as a named field. The workshop brought researchers together around the prospect of machine intelligence.
Natural language processing : The field of enabling computers to analyze, generate, and interact through human language. Language technologies apply AI to translation, search, dialogue, and text generation.
Explainable artificial intelligence : Methods that make an AI system's outputs or behavior more understandable to people. Explanations can help assess decisions made by complex models.
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