During this time, we’ve seen how expertise in other scientific fields like theoretical physics and bioengineering can yield insights to push AI research forward. All 6 Fellows have authored or contributed to papers and completed projects investigating a novel research idea while embedded in an OpenAI research team.
Research projects from our next class of Fellows are underway and we are in the process of selecting our next cohort. We’re also excited to welcome a number of our Fellows to OpenAI as full-time members of our technical staff.
Final projects
Karl Cobbe






Sam McCandlish
The tradeoff between experience and training time needed to achieve a given score is predictable.
Yilun Du
My second project was on exploring how to scale and stabilize training of energy based models. With these tricks, I found that energy based models generated much better samples than other state-of-the-art likelihood models. I found that energy based models exhibited good likelihoods model and are able to inpaint and restore test CIFAR-10 samples. I further found that energy based models generalized well, showing state-of-the-art out-of-distribution generalization, compositional ability, and lower long term trajectory prediction error.
Josh Meier
Johannes Otterbach
Xingyou (Richard) Song
Next steps
We’d like to congratulate our Summer 2018 Fellows on their outstanding work and thank them for their contributions to OpenAI. We are excited to see what research they publish next!
As part of our effort to educate and attract more people like our class of Fellows, we recently open sourced part of their introductory curriculum. You can start your ML education today by completing our tutorial, “Spinning up in Deep RL.” Spinning up consists of crystal-clear examples of RL code, educational exercises, documentation, and tutorials that will help you become a skilled practitioner in RL.
Applications for our 2019 Winter Fellows Cohort have closed—please stay tuned for our next call for applications later in 2019.