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Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
On the other hand, a problem is AI-Hard if and only if there is an AI-Complete problem that is polynomial time Turing-reducible to . This also gives as a consequence the existence of AI-Easy problems, that are solvable in polynomial time by a deterministic Turing machine with an oracle for some problem.
Generative artificial intelligence (generative AI, GenAI, [167] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 168 ] [ 169 ] [ 170 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 171 ...
A problem is informally called "AI-complete" or "AI-hard" if it is believed that in order to solve it, one would need to implement AGI, because the solution is beyond the capabilities of a purpose-specific algorithm. [47] There are many problems that have been conjectured to require general intelligence to solve as well as humans.
Elements of AI is a massive open online course (MOOC) teaching the basics of artificial intelligence. [1] The course, originally launched in 2018, is designed and organized by the University of Helsinki and learning technology company MinnaLearn . [ 2 ]
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Gen Z was born between 1997 and 2012 and is considered the first generation to have largely grown up using the internet, modern technology and social media.
AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...