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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. [40] For example, one version of OpenAI's GPT-4 accepts both text and image inputs. [41]
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] 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.
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
The AIs learn a generative model (hence their name) that enables them to predict patterns in various kinds of data or signal. What generative there means is that they learn enough about the deep ...
The tech industry is throwing generative AI at everything it can think of to see what sticks in the hopes of developing the next big thing. Generative AI is having a throw-everything-at-the-wall ...
For me personally, one of the most fundamental use cases for generative AI thus far seems to be email. Which is why I pinged Rahul Vohra, the CEO of Superhuman, a few weeks ago.
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