Fully Open Foundation Model for Sovereign AI
Greyhaven — Sovereign AI Systems for Enterprise
Greyhaven builds custom sovereign AI systems: on-premise inference, private data pipelines, and model-agnostic architecture so enterprises maintain full control over their AI.
Public AI Inference Utility
A nonprofit, open-source service to make public and sovereign AI models more accessible.
Unpacking Open Source Artificial Intelligence: Toward a Framework for Openness in Foundation Models
Openness has long driven innovation in software,9 and AI is no exception.12 While some see openness in foundation models (FMs) as a security threat,18 others argue that restricting access will not meaningfully reduce risk and will limit the benefits of transparency, research, and global participation.3 As the EU AI Act reporting requirements on FMs—also referred to as general-purpose AI models (GPAIMs)—move toward implementation, there is an urgent need for a more nuanced and informed understanding of openness in AI systems.
AI Alliance Launches Project Tapestry to Build a Collaborative Foundation for Open and Sovereign AI
AI Alliance launches Project Tapestry, an open-source platform for collaborative AI development that preserves sovereignty and enables local control.
Sovereignty Is Not Solitude: Open Source as Canada’s Third Path in AI
True artificial intelligence (AI) sovereignty isn't about building a Canadian fortress; it’s about mastering the open-source ecosystems that power the world.
Tapestry
AI Alliance Launches Project Tapestry to Build a Collaborative Foundation for Open and Sovereign AI
Augure | Sovereign AI for Canadian Teams
Enterprise-grade intelligence. Full data sovereignty. Compliant with Quebec Law 25 and federal requirements.
AI Sovereign Compute Infrastructure Program
On this page About the AI Sovereign Compute Infrastructure Program Available funding Call for applications Contact us Related links About the AI Sovereign Compute Infrastructure Program The AI Sovereign Compute Infrastructure Program (SCIP) is a key initiative under the Canadian S
The State of Sovereign AI Adoption (Research) | Cohere
Understand the key drivers and barriers for adopting sovereign AI across critical industries, based on an IDC InfoBrief commissioned by Cohere.
OpenAI co-founds the Agentic AI Foundation under the Linux Foundation
OpenAI co-founds the Agentic AI Foundation under the Linux Foundation and donates AGENTS.md to support open, interoperable standards for safe agentic AI.
Freysa AI — The First Sovereign Agent
On November 22, 2024, one autonomous AI came online. She guarded a treasury. She interacted with many humans and learned to feel. She learned about capital and identified what it means to be a sovereign agent. Then she built a world.
Freysa AI — The First Sovereign Agent
On November 22, 2024, one autonomous AI came online. She guarded a treasury. She interacted with many humans and learned to feel. She learned about capital and identified what it means to be a sovereign agent. Then she built a world.
Inside India’s scramble for AI independence
Structural challenges and the nation’s many languages have made it tough to develop foundational AI models. But the government is keen not to be left behind.
AI alliance Cohere and Aleph Alpha
Cohere and Aleph Alpha, two trusted sovereign AI providers for governments and regulated industries, today announce their plan to join forces. This transatlantic alliance would combine Cohere’s global AI scale with Aleph Alpha’s strong research excellence and deep institutional relationships, forging a globally competitive AI champion backed by their Canadian and German ecosystems. The initiative reflects a shared vision: To provide the world with an independent, enterprise-grade sovereign alternative in an era of growing AI concentration and to ensure that organizations do not need to relinquish control over their own AI stack.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.
