Announcing the first AI Grant Fellows
Last month, I decided to give five grants of $5,000 each to people pursuing open source AI projects (a few days later, five grants became ten, thanks to a generous contribution from Ann Miura-Ko at Floodgate).
Over the course of the next two weeks, more than 450 people wrote in about their incredible projects.
To all of you who applied, it was extremely stimulating to read your applications. They covered so many fields, and even as an AI optimist, I was honestly quite surprised at how many domains AI in general and deep learning in particular are already transforming.
Naturally, we received a lot of applications in tooling, NLP, and other core areas. But so many of you submitted your ideas in biology, physics, materials science, sociology, astronomy, literature, journalism, animal husbandry, and many, many other fields. It was not boring.
But it was extremely hard to choose the fellows. Thankfully, more than two dozen experts volunteered to help, and spent more than 100 hours writing and submitting more than 2,000 individual reviews. My most humble thanks to all the reviewers, without whom this project would have been impossible.
We first picked 46 finalists, and asked each to submit a 2-minute video with more detail about their project. From those videos, the ten winners were chosen.
And so I am thrilled to present the first ten AI Grant Fellows:
- Juan Carrasquilla, simulation of many-body quantum systems with neural networks (video).
- Natalia Mykhaylova, training datasets and source identification algorithms for sensor networks that improve public health (video).
- Oliver Hennigh, predicting steady-state fluid flow using deep neural networks (video).
- Russell Kaplan and Christopher Sauer, natural language guided reinforcement learning (video).
- Kevin Kwok, a fast, cross-platform library for hardware-accelerated deep learning in the browser using WebGL (video).
- Liam Atkinson, a neural network to generate puns (video).
- Jordi Pons Puig, labeling the Freesound dataset using the AudioSet ontology (video).
- Patrick Slade, machine learning for motion recognition and trajectory generation of human movement for rehabilitation (video).
- Manasi Vartak, ModelDB: a system to manage machine learning models (video).
- Mark Wronkiewicz, simulating physiologically plausible human brain electromagnetic activity using GANs (video).
In addition to the $5,000 cash grants, thanks to the generous sponsorship of some great companies, each Fellow will also receive:
- from CrowdFlower, $5,000 in human labeling credits.
- from Microsoft, $1,000 in Azure credits.
- from ScaleAPI, $1,000 in human labeling credits.
- from FloydHub, 250 hours on a K80 GPU.
Sincere thanks to each of the sponsors for their support.
And congratulations to the Fellows! I’m so happy to have gotten to know all of you during this process, and I’m looking forward to what you do next.
And what’s next for AI Grant? Happily, I can report that there will be additional rounds of grants offered in the future. Fairly soon, actually. If you’re interested in staying informed, please sign up for our mailing list.
