Accelerating responsible innovation in AI
Open and transparent innovation is essential for equipping AI researchers, developers, and users with the knowledge and tools to leverage AI advancements safely and inclusively, prioritizing diversity and economic opportunity for all.
Through member-driven working groups, we bring together builders and experts from various fields to collaboratively and transparently address the challenges of generative AI and democratize its benefits.
Contribute to an AI Alliance projectCore projects
View allCore projects address substantial cross-community challenges and are an opportunity for individual contributors and members to collaborate, build, and make an impact on the future of AI. Core Projects are managed directly by the AI Alliance and governed as described below.
A statement in opposition to California SB 1047
Advocacy
Our perspectives and recommendations in opposition to California SB 1047, the proposed Safe and Secure Innovation for Frontier Artificial Intelligence Models Act.
Trusted evals request for proposals
Trust & Safety
The AI Alliance Trusted Evals request for proposals is aimed at seeking new perspectives in the AI evaluation domain. We are excited to work with those in academia, industry, startups and anyone excited to collaborate in the open and build an ecosystem around their work.
Responding to the U.S. NTIA request for comment on Dual Use Foundation Artificial Intelligence Models with Widely Available Model Weights
Advocacy
The request seeks public input on the potential risks, benefits, and policy approaches for AI foundation models whose weights are broadly accessible.
Affiliated projects
View allAffiliated projects are led and managed by members and identified as being aligned to the AI Alliance mission. Although Affiliated Projects are not managed by the AI Alliance, we highlight them as contributing to open, safe, and responsible AI.
IBM Granite Code Models
A series of code models trained by IBM licensed under Apache 2.0 license. Includes base pretrained and instruct models.
AIConfig & Editor
AIConfig is a config-based framework for building generative AI applications.
GenAI in Education: Usage Guidance
A report evaluating the feasibility, benefits, and limitations of using generative AI technologies in an educational setting and its impact on learning outcomes.
InstructLab
InstructLab is a model-agnostic open source AI project that facilitates contributions to Large Language Models (LLMs).
LLM 360: Amber
The Amber project includes a 7B English language model with the LLaMA architecture, an instruction-following model finetuned from LLM360-Amber, and a safety-finetuned instruction model using LLM360-AmberChat as the base.
TrustyAI
TrustyAI is an open-source toolkit designed for responsible AI.
Ragna
Ragna is an open-source RAG-based (Retrieval-Augmented Generation) AI orchestration framework designed to bridge the gap between AI research and production deployment.