Salesforce, in cooperation with Hugging Face, Cohere, and Carnegie Mellon University, today launched the AI Energy Score. This groundbreaking benchmarking tool allows AI developers and consumers to evaluate, identify, and compare the energy usage of AI models.
Salesforce additionally declared that it will be the first AI model developer to share the energy efficiency data for its proprietary models under the new framework.
The AI Energy Score is intended to address the lack of transparency on the environmental impact of AI models. Similar to how Energy Star altered energy efficiency requirements appliances and electronics, this program offers a clear, reliable benchmark for AI model sustainability.
The AI Energy Score will make its premiere at the AI Action Summit, which will bring together leaders from over 100 countries, the commercial sector, and civil society to discuss how to use artificial intelligence for good.
By increasing transparency, the score can influence market demand for efficient models and stimulate long-term AI development. Recognized by the French Government and the Paris Peace Forum for its transformative potential, the AI energy score includes:
Standardized Energy Ratings: A methodology for testing and comparing the energy efficiency of artificial intelligence models.
Public Leaderboard: A complete leaderboard with scores for ten typical AI tasks, such as text generation, image generation, and summarization, performed by 166 models, including Salesforce’s SFR-Embedding, xLAM, and SF-TextBase.
Benchmarking Portal: A platform that allows AI developers to submit open or proprietary AI models for evaluation and inclusion on the leaderboard. Open models may be tested automatically, whereas closed models can be evaluated via a secure testing sandbox.
Recognizable Energy Use Label: A new 1- to 5-star rating system for AI model energy use, with five stars signifying the highest efficiency. This enables developers and users to easily identify and select more sustainable models.
Once graded, AI developers can build standardized labels to communicate their models’ energy scores, with built-in recommendations on how to display the labels for maximum impact.
The company introduced Agentforce, Salesforce Platfom’s agentic layer for deploying autonomous AI agents across any business function. Agentforce provides tools for creating and customizing agents, as well as a library of pre-built skills for sales, services, marketing, commerce, Tableau, Slack, and other areas.
This is the end of this article, we hope you cover all the complete information about this Salesforce Collaboration. For more updates and Salesforce news follow us at CloudMetic.
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