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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 ...
Books help generative AI systems with learning how to communicate information. Some AI training text can be pulled from articles that are posted on the internet, but high-quality AI requires high ...
Ian J. Goodfellow (born 1987 [1]) is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning.He is a research scientist at Google DeepMind, [2] was previously employed as a research scientist at Google Brain and director of machine learning at Apple as well as one of the first employees at OpenAI, and has made several ...
Liang Zhao is a computer scientist and academic. He is an associate professor in the Department of Computer Science at Emory University. [1]Zhao's research focuses on data mining, machine learning, and artificial intelligence, with particular interests in deep learning on graphs, societal event prediction, interpretable machine learning, multi-modal machine learning, generative AI, and ...
Generative literature is poetry or fiction that is automatically generated, often using computers. It is a genre of electronic literature , and also related to generative art . John Clark 's Latin Verse Machine (1830–1843) is probably the first example of mechanised generative literature, [ 1 ] [ 2 ] while Christopher Strachey 's love letter ...
Artificial Intelligence: A Guide for Thinking Humans is a 2019 nonfiction book by Santa Fe Institute professor Melanie Mitchell. [1] The book provides an overview of artificial intelligence (AI) technology, and argues that people tend to overestimate the abilities of artificial intelligence.
Pages in category "Non-fiction books about Artificial intelligence" The following 23 pages are in this category, out of 23 total.
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 ...