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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 ...
Gato is a deep neural network for a range of complex tasks that exhibits multimodality.It can perform tasks such as engaging in a dialogue, playing video games, controlling a robot arm to stack blocks, and more.
The training compute of notable large AI models in FLOPs vs publication date over the period 2017-2024. The majority of large models are language models or multimodal models with language capacity. Before 2017, there were a few language models that were large as compared to capacities then available.
Google VP Sissie Hsiao called the multimodal capabilities of Gemini the “most visually stunning” of the model’s advancement while onstage at Fortune’s Brainstorm AI event yesterday (more ...
In April 2023, Huawei released a paper detailing the development of PanGu-Σ, a colossal language model featuring 1.085 trillion parameters. Developed within Huawei's MindSpore 5 framework, PanGu-Σ underwent training for over 100 days on a cluster system equipped with 512 Ascend 910 AI accelerator chips, processing 329 billion tokens in more than 40 natural and programming languages.
Generative Pre-trained Transformer 4 (GPT-4) is a multimodal large language model trained and created by OpenAI and the fourth in its series of GPT foundation models. [1] It was launched on March 14, 2023, [1] and made publicly available via the paid chatbot product ChatGPT Plus, via OpenAI's API, and via the free chatbot Microsoft Copilot. [2]