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A text-to-video model is a machine learning model that takes a natural language description as input and produces a video relevant to the input text. [1] Recent advancements in generating high-quality, text-conditioned videos have largely been driven by the development of video diffusion models. [2]
Artificial general intelligence – Human-level or stronger AI for a wide range of tasks. Artificial imagination – Artificial simulation of human imagination. Artificial intelligence art – Machine application of knowledge of human aesthetic expressions. Artificial life – Field of study.
Critics compared Dream Machine heavily to Sora, a text-to-video model created by OpenAI, and Kling, another text-to-video model, upon its release. [2] [11] Charles Pulliam-Moore of The Verge wrote that "bullish fans" of generative AI "were quick to call [Dream Machine] a novel innovation", but remarked upon its training data not being available ...
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v. t. e. Sora is an upcoming generative artificial intelligence model developed by OpenAI, that specializes in text-to-video generation. The model generates short video clips corresponding to prompts from users. Sora can also extend existing short videos. As of August 2024 it is unreleased and not yet available to the public.
Gen-2 is a multimodal AI system that can generate novel videos with text, images or video clips. The model is a continuation of Gen-1 and includes a modality to generate video conditioned to text. Gen-2 is one of the first commercially available text-to-video models. [28] [29] [30] [31]
In the context of AI, it is particularly used for embedded systems and robotics. Libraries such as TensorFlow C++, Caffe or Shogun can be used. [1] JavaScript is widely used for web applications and can notably be executed with web browsers. Libraries for AI include TensorFlow.js, Synaptic and Brain.js. [6]
GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [3] [4] [5] GPT-2 was created as a "direct scale-up" of GPT-1 [6] with a ten-fold increase in both its parameter count and the size of its training dataset. [5]