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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 ...
Generative science is an area of research that explores the natural world and its complex behaviours. It explores ways "to generate apparently unanticipated and infinite behaviour based on deterministic and finite rules and parameters reproducing or resembling the behavior of natural and social phenomena". [ 1 ]
The development of recursive self-improvement raises significant ethical and safety concerns, as such systems may evolve in unforeseen ways and could potentially surpass human control or understanding. There has been a number of proponents that have pushed to pause or slow down AI development for the potential risks of runaway AI systems. [3] [4]
General game playing – Ability of artificial intelligence to play different games; Generative artificial intelligence – AI system capable of generating content in response to prompts; Human Brain Project – Scientific research project; Intelligence amplification – Use of information technology to augment human intelligence (IA)
The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
Generative concern leads to concrete goals and actions such as "providing a narrative schematic of the generative self to the next generation". [7] McAdams and de St. Aubin developed a 20-item scale to assess generativity, and to help discover who it is that is nurturing and leading the next generation. [4]
IA is sometimes contrasted with AI (artificial intelligence), that is, the project of building a human-like intelligence in the form of an autonomous technological system such as a computer or robot. AI has encountered many fundamental obstacles, practical as well as theoretical, which for IA seem moot, as it needs technology merely as an extra ...
The first energy-based generative neural network is the generative ConvNet proposed in 2016 for image patterns, where the neural network is a convolutional neural network. [10] [11] The model has been generalized to various domains to learn distributions of videos, [7] [2] and 3D voxels. [12] They are made more effective in their variants.