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Gemini - A Family of Highly Capable Multimodal Models: Abstract and Introduction

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Manage episode 391566067 series 3474385
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/gemini-a-family-of-highly-capable-multimodal-models-abstract-and-introduction.
Gemini - A Family of Highly Capable Multimodal Models: Abstract and Introduction
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #gemini, #generative-ai, #google-gemini, #multimodal-models, #multimodal-llm, #hackernoon-top-story, #hackernoon-scholar, and more.
This story was written by: @escholar. Learn more about this writer by checking @escholar's about page, and for more stories, please visit hackernoon.com.
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks — notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of Gemini models in cross-modal reasoning and language understanding will enable a wide variety of use cases and we discuss our approach toward deploying them responsibly to users.

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397 episodes

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Manage episode 391566067 series 3474385
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/gemini-a-family-of-highly-capable-multimodal-models-abstract-and-introduction.
Gemini - A Family of Highly Capable Multimodal Models: Abstract and Introduction
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #gemini, #generative-ai, #google-gemini, #multimodal-models, #multimodal-llm, #hackernoon-top-story, #hackernoon-scholar, and more.
This story was written by: @escholar. Learn more about this writer by checking @escholar's about page, and for more stories, please visit hackernoon.com.
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks — notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of Gemini models in cross-modal reasoning and language understanding will enable a wide variety of use cases and we discuss our approach toward deploying them responsibly to users.

  continue reading

397 episodes

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