Artificial Intelligence — definition?
Simulation of human intelligence processes by machines.
History of AI — start?
Mid-20th century with initial optimism and setbacks.
Narrow AI — role?
Designed for specific tasks like voice recognition.
General AI — hypothetical?
Yes, capable of human-like understanding across tasks.
Turing Test — purpose?
Evaluate if a machine's behavior is indistinguishable from human.
Machine Learning — definition?
Systems learning from data to improve performance.
AI vs ML — difference?
AI is broader; ML uses data-driven algorithms within AI.
Supervised Learning — data type?
Labeled data with known outputs.
Unsupervised Learning — data?
Unlabeled data to find patterns.
Deep Learning — key feature?
Uses multi-layer neural networks for complex patterns.
Neural Network — basic unit?
Neuron that processes inputs and passes outputs.
Natural Language Processing — goal?
Enable computers to understand and generate human language.
Computer Vision — task?
Interpret and analyze visual information from images or videos.
AI applications — sectors?
Healthcare, finance, autonomous vehicles, and more.
Reinforcement Learning — mechanism?
Learns via rewards and penalties through trial and error.
Overfitting — problem?
Model learns noise, performs poorly on new data.
Clustering — purpose?
Group data based on similarity without labels.
Activation Function — role?
Introduce non-linearity into neural networks.
Testez vos connaissances avec un QCM de 9 questions sur Introduction to AI and Machine Learning.
1. In what year did Alan Turing propose the Turing Test as a measure of AI's human-like ability?
2. Who proposed the Turing Test as a benchmark for evaluating artificial intelligence?
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