Our members are building the future of AI
These affiliated projects represent some of the most significant efforts behind the movement to create safe, responsible AI rooted in open innovation.
Affiliated Projects
A new hue angle to measure apparent skin tone in computer vision.
A new hue angle - which spans the range from red to yellow - to measure apparent skin color in computer vision beyond a unidimensional scale on skin tone.
AI Education - AI for the Workforce
Community college education program for broad AI Education
AI Safety Leaderboard
A leaderboard to identify the most effective models for a users specific use case.
AI methods for network design
A computationally efficient AI method (drawn from optimal learning and approximate dynamic programming fields) to solve sequential knapsack problems.
AI policy recommendations
Policy pieces on AI.
AIConfig & Editor
AIConfig is a config-based framework for building generative AI applications.
Among Us
A project to investigate the capabilities of base LLMs as well as evaluate their capacity to pose as human participants
Bactrian-X
A Multilingual Replicable Instruction-Following Model
Benchmarks for sensory grounding in LLMs
Benchmarks for algorithms that transcend limits and bring LLMs closer to real human-like reasoning and understanding.
BiMediX - Bilingual Medical Mixture of Experts LLM
BiMediX is the first bilingual medical mixture of experts LLM designed for seamless interaction in both English and Arabic. The model facilitates a wide range of medical interactions in English and Arabic, including multi-turn chats to inquire about additional details such as patient symptoms and medical history, multiple-choice question answering, and open-ended question answering.
Bias Detection to Care Protection
Iterative feedback mechanisms and the integration of fairness metrics into the evaluation process further amplify the potential impact of this project.
BigCode
BigCode is an open scientific collaboration working on the responsible development and use of large language models for code (Code LLMs), empowering the machine learning and open source communities through open governance.