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The question is based on two concepts: First artificial intelligence (AI) The transistor is an intelligent device. Let us talk about the first AI, why transistors, the same definition of intelligence can be applied to Vaccum tubes, and they definitely existed before transistors. So no matter what definition you decide for intelligence, transistors are not ...


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Learning algorithms (some others too, like search) aim at generating functions that get as close as possible to the "shape" of the training data (so we can then feed values to the generated functions and get outputs like, say, a prediction). In 2D, the "shape" may be easy to visualize. If the data in 2D seems to line up, learning ...


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GANs are usually trained in a self-supervised fashion, i.e. they use the unlabelled data as the supervisory signal. Note that some self-supervised learning methods are unsupervised learning techniques, given that no human-annotated data is needed. However, not all SSL techniques are used for solving an unsupervised learning task. In fact, there are SSL ...


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The term was introduced to the machine learning and computer science community by Rina Dechter in Learning While Searching in Constraint-Satisfaction-Problems (1986) [1], where she writes Discovering all minimal conflict-sets amounts to acquiring all the possible information out of a dead-end. Yet, such deep learning may require considerable amount of work. ...


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The term it comes from cognitive science - depending on the paradigm, it can have many meanings correlated with neuroscience as well as the brain. Activities based on this sphere is : language learning thinking perception awareness decision making intelligence The mentioned cognitive processes - activate the appropriate regions in the brain narrow ...


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A good example is the degree of freedom in Student's distribution: ‌ The degrees of freedom refers to the number of independent observations in a set of data. For example: When estimating a mean score or a proportion from a single sample, the number of independent observations is equal to the sample size minus one. e.g, if we have 100 observation $X_1, \...


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