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Artificial intelligence

artificial intelligence

Subject: 
English
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Futurology
Computational neuroscience
Singularitarianism
Artificial intelligence
Cybernetics
Superintelligence
Attachment theory
Ai

Artificial intelligence (AI) refers to the ability of a computer to perform functions in a manner analogous to human decision making and learning. For instance, the GPS systems use automated learning, perception, reasoning, and understanding to find the best route that a user should take from several possible alternatives. However, the increase in effectiveness of AI?s has resulted in concerns about the potential risks that may result from such advances. Some of the concerns are about the possibility of the AI?s becoming ?superintelligent? and thus threatening the survival of humankind. Notably, just like ordinary softwares, AI softwares are subject to programming errors. Therefore, if these errors occur in sensitive AI applications such as health care, they may result in

Scenarios

Subject: 
Art History
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Artificial intelligence
Educational psychology
Neuropsychological assessment
Problem solving
Operations research

Problem Solving Scenarios Directions: Choose one of the scenarios listed below or develop one of your own with your teacher?s approval. Complete the problem solving guide sheet. Suppose you have decided to buy a new vehicle. You are undecided on what type to buy. You need something affordable both in payments and gas mileage and one that has enough room for the driver and at least three passengers. You will occasionally need to pull your family?s fishing boat. You also want to choose something safe and dependable. Each type of vehicle has pros and cons; what would you decide to purchase?

data mining

Subject: 
Statistics
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Ensemble learning
Statistics
Artificial intelligence
learning
AdaBoost
Statistical classification
Classifier
Boosting
Margin classifier

CS 301 ? Intro to Data Mining Ensemble Methods (continued) ? Use multiple classifier models (built using a single method like ID3) to obtain better predictive performance than could be obtained from any of the individual classifier models ? Methods for Constructing an Ensemble Classifier (1) Specify classifier method you want to use (e.g., ID3, PRISM, etc.) (2) Manipulate the training dataset according to some strategy - multiple training sets created by resampling the original data according to some sampling distribution (3) Build a base classifier from each training set (using the classifier method you specified) (4) Construct an ensemble classifier by considering how the base classifiers would make predictions on original dataset (consensus opinion)
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