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An intelligent decision support system (IDSS) is a decision support system that makes extensive use of artificial intelligence (AI) techniques. Use of AI techniques in management information systems has a long history – indeed terms such as "Knowledge-based systems" (KBS) and "intelligent systems" have been used since the early 1980s to describe components of management systems, but the term ...
The capabilities of a generative AI system depend on the modality or type of the data set used. Generative AI can be either unimodal or multimodal; unimodal systems take only one type of input, whereas multimodal systems can take more than one type of input. [59] For example, one version of OpenAI's GPT-4 accepts both text and image inputs. [60]
Generative systems are technologies with the overall capacity to produce unprompted change driven by large, varied, and uncoordinated audiences. [1] When generative systems provide a common platform, changes may occur at varying layers (physical, network, application, content) and provide a means through which different firms and individuals may cooperate indirectly and contribute to innovation.
Artificial intelligence is also starting to be used in video production, with tools and software being developed that utilize generative AI in order to create new video, or alter existing video. Some of the major tools that are being used in these processes currently are DALL-E, Mid-journey, and Runway. [ 248 ]
Most artificial intelligence systems involve some sort of integrated technologies, for example, the integration of speech synthesis technologies with that of speech recognition. However, in recent years, there has been an increasing discussion on the importance of systems integration as a field in its own right.
This understanding of AI as a technology and its relationship with humans is a striking departure from the traditional vision of full automation that has successfully propelled the introduction of ...
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]
For example, GPT-3, and its precursor GPT-2, [11] are auto-regressive neural language models that contain billions of parameters, BigGAN [12] and VQ-VAE [13] which are used for image generation that can have hundreds of millions of parameters, and Jukebox is a very large generative model for musical audio that contains billions of parameters.
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