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AI Grant Open Source AI Projects Program is a grant from AI Grant (Nat Friedman and Daniel Gross) that funds open source artificial intelligence and machine learning projects. Established in 2017, the program awards $5,000 to $50,000 per project with no strings attached; grants can be issued as cash or compute credits.
Funded projects span a wide range of AI applications including language models, computer vision, drug discovery, medical imaging, and AI tools for researchers. Projects must be publicly available and contribute to the open source AI ecosystem. Individuals and teams working on open source AI projects are eligible.
Past grantees have included contributors to llama-cpp-python, the GGUF file format, and numerous machine learning research and tooling projects.
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AI Grant — grants for open source projects Looking for our accelerator program? Check out aigrant. com !
$5,000 - $50,000 in grants for open source projects, no strings attached. Grants can come in the form of compute or cash. abetlen – for their work on llama-cpp-python.
philpax – for their work on the GGUF file format. TySam – for their work on 10-second models. Russell Kaplan and Christopher Sauer , to build and open source an RL agent that learns faster because you can talk to it based on our prototype that beats most other approaches to Montezuma’s Revenge ( paper ).
Kevin Kwok , a fast cross-platform library for hardware-accelerated deep learning in the browser using WebGL ( video ). Jordi Pons , the freesound datasets project ( video ). Patrick Slade , machine learning for motion recognition and trajectory generation of human movement for rehabilitation ( video ).
Oliver Hennigh , predicting steady state fluid flow using deep neural networks ( video ). Manasi Vartak , a system to manage machine learning models ( video ). simulation of many-body quantum systems with neural networks ( video ).
Liam Patrick Atkinson , a neural network to generate puns ( video ). Natalia Mykhaylova , training datasets and source identification algorithms for sensor networks that improve public health ( video ). Mark Wronkiewicz , Majid Mirbagheri and Nicholas Foti , to simulate human brain activity using tools recently development in machine learning ( video ).
Zbigniew Wojna (co-author of Inception-v3, one of the first better-than-humans perception models), object detection and instance segmentation for small objects ( paper ). Flora Ponjou Tasse , turning hand-drawn sketches into 3D objects using generative models ( video ). Radim Rehurek , is going to make gensim (hugely popular open-source library for topic modeling) support many of the latest-and-greatest research papers ( video ).
Byron Knoll, author of cmix , a library that uses deep learning to compress files ( video ). Brian Nord , for using AI to model the physics of strong gravitational lensing ( video ). Samuel Lee , Neal Jean , Tracey Hong and Feiya Shao , Bob Zheng , will make neural networks that detect child abuse in X-Rays ( video ).
Darius Barušauskas , AI to assist doctors interpreting brain stroke scans with 3D Computerized Tomography ( video ). Hannah Davis , creating a dataset of sceneries that evoke different emotional responses ( video ). David Koes , AI that checks for docking of various drugs to accelerate structure-based drug design ( video ).
A. Mira Chung and Hooyeon Lee , use DL to generate art for video games ( video ). Sarah Newman , a series of thought experiments about human values in speculative AI futures ( video ).
Alex Wang , AI that protects you from face recognition systems ( video ). Aidan Gomez , cipher cracking(!) using generative adversarial neural networks ( video ).
Ranjay Krishna , extracting object and relationship classifications from video ( video ). Kaden Hazzard , predicting quantum dynamics from short-time dynamics using machine learning ( video ). Ariel Kanevsky , a DNN algorithm capable of analyzing free tissue transfers and detect abnormal vascular flow within blood vessels ( video ).
Jake Bian , Firebug, for deep learning ( video ). Daniel Soudry , a neural network that predicts the validation error of another neural network ( video ). Tejpal Virdi , John Guibas and Peter Li , use GANs to generate usable and privacy preserving training data.
Ekta Prashnani , a metric to assess image quality consistent with human perception of image quality. Established in 2017 by Nat Friedman and Daniel Gross .
Based on current listing details, eligibility includes: Open source projects working on artificial intelligence, machine learning, and related technologies. Projects should be publicly available and contribute to the open source AI ecosystem. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates $5,000 - $50,000 Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
The current target date is rolling deadlines or periodic funding windows. Build your timeline backwards from this date to cover registrations, approvals, attachments, and final submission checks.
Federal grant success rates typically range from 10-30%, varying by agency and program. Build a strong proposal with clear objectives, measurable outcomes, and a well-justified budget to improve your chances.
Requirements vary by sponsor, but typically include a project narrative, budget justification, organizational capability statement, and key personnel CVs. Check the official notice for the complete list of required attachments.
Yes — AI tools like Granted can help research funders, draft proposal sections, and check compliance. However, always review and customize AI-generated content to reflect your organization's unique strengths and the specific requirements of the solicitation.
Review timelines vary by funder. Federal agencies typically take 3-6 months from submission to award notification. Foundation grants may be faster, often 1-3 months. Check the program's timeline in the official solicitation for specific dates.
Many federal programs offer multi-year funding or allow competitive renewals. Check the official solicitation for continuation and renewal policies. Non-competing continuation applications are common for multi-year awards.
Academic Grant Program is sponsored by NVIDIA. NVIDIA's Academic Grant Program seeks proposals from full-time faculty members at accredited academic institutions using NVIDIA technology to advance work in Simulation and Modeling, Data Science, and Robotics and Edge AI. Proposals for the NVIDIA Graduate Fellowship Program are also invited, focusing on AI, robotics, and autonomous vehicles.
Manufacturing USA Institute – AI for Resilient Manufacturing is sponsored by National Institute of Standards and Technology (NIST). NIST is seeking applications to establish and operate a Manufacturing USA institute focused on leveraging artificial intelligence to strengthen the resilience of U.S. manufacturers, particularly concerning supply chain networks. The institute will conduct applied R&D projects and cultivate a skilled workforce.