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There are several architectures that have been used to create Text-to-Video models. Similar to Text-to-Image models, these models can be trained using Recurrent Neural Networks (RNNs) such as long short-term memory (LSTM) networks, which has been used for Pixel Transformation Models and Stochastic Video Generation Models, which aid in consistency and realism respectively. [31]
Nvidia has developed a new kind of artificial intelligence model that can create sound effects, change the way a person sounds, and generate music using natural language prompts.Called Fugatto, or ...
Large language model – Type of machine learning model; Music and artificial intelligence – Usage of artificial intelligence to generate music; Generative AI pornography – Explicit material produced by generative AI; Procedural generation – Method in which data is created algorithmically as opposed to manually
Understanding Music with AI: Perspectives on Music Cognition Archived 2021-01-10 at the Wayback Machine. Edited by Mira Balaban, Kemal Ebcioglu, and Otto Laske. AAAI Press. Proceedings of a Workshop held as part of AI-ED 93, World Conference on Artificial Intelligence in Education on Music Education: An Artificial Intelligence Approach
Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices.
Here’s Epoch AI’s projection of the hardware cost involved in training the most expensive AI models, through 2030. This excludes AI researchers’ salaries, which are considerable these days ...
The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data. [2]
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...