Samsung Gauss AI Model Revealed at AI Forum 2023

Samsung Gauss AI Model Revealed at AI Forum 2023

Published: November 08, 2023

Samsung has just introduced its own generative AI model, Samsung Gauss, named after mathematician Carl Friedrich Gauss known for the Normal Distribution Theory (Gaussian) that underpins AI and machine learning.

The tech giant made the announcement at the Samsung AI Forum 2023 held at its R&D campus in Seoul, South Korea.

Samsung Gauss is the culmination of the company's vision for generative AI and was crafted by Samsung Research.

Currently deployed internally, Samsung plans to offer Gauss to the public through its future products.

Samsung Gauss comprises three components:

  • Samsung Gauss Language
  • Samsung Gauss Code
  • Samsung Gauss Image
Samsung Gauss AI model
Samsung Gauss Components | Source: Samsung

Products leveraging these generative AI models are expected to launch in the upcoming months, potentially featuring in the Galaxy S24 series set for release in January 2024.

Samsung Gauss Language understands and generates human language, aiding tasks like email composition, document editing, translation and writing.

It promises smarter device control when integrated into products and functions across both cloud servers and endpoint devices such as phones, tablets, and PCs.

On the other hand, Samsung Gauss Code is tailored for code languages and includes "code.i," a code assistant primarily for internal Samsung use.

It streamlines software development with features for code description and test case generation.

Samsung Gauss Image simplifies image creation and editing by altering styles, enhancing image resolution, and generating images from text descriptions, reminiscent of Dall-E's capabilities.

To protect private data, all Samsung Gauss Generative AI models can operate in on-device mode.

Samsung has also established an AI Red Team, adhering to AI ethics principles, to internally assess privacy and security aspects throughout the AI development process, from data collection to result formation.

Edited by Nikola Djuric

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