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Generative artificial intelligence (generative AI, GenAI, [166] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 167 ] [ 168 ] [ 169 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 170 ...
Artificial general intelligence (AGI) is a type of artificial intelligence (AI) that matches or surpasses human cognitive capabilities across a wide range of cognitive tasks. This contrasts with narrow AI , which is limited to specific tasks. [ 1 ]
Asilomar Conference on Beneficial AI was held, to discuss AI ethics and how to bring about beneficial AI while avoiding the existential risk from artificial general intelligence. Deepstack [121] is the first published algorithm to beat human players in imperfect information games, as shown with statistical significance on heads-up no-limit poker.
A hypothetical artificial general intelligence (AGI) that would have a positive effect on humanity. It is a part of the ethics of artificial intelligence and is closely related to machine ethics. While machine ethics is concerned with how an artificially intelligent agent should behave, friendly artificial intelligence research is focused on ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
He has also authored multiple books on artificial intelligence and its potential promise and peril. In December 2012 Kurzweil was hired by Google in a full-time director of engineering position to "work on new projects involving machine learning and language processing". [ 54 ]
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 computer vision. [7]
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. [2] [3]