August 1, 2019
Learning Day
At OpenAI, each Thursday is Learning Day: a day where employees have the option to self-study technical skills that will make them better at their job but which aren’t being learned from daily work.
Illustration: Ben Barry
We’ve found that the biggest contributions at OpenAI come from cross-functional experts, so we either need to hire them or grow them here. Before Learning Day, we very rarely saw people grow cross-functionally—for example, employees coming from a software background rarely picked up machine learning (something equally rare in other organizations except academia). Since Learning Day, this kind of growth has become very common.
On a typical learning day, people do things like:
- Reimplement papers.
- Follow deep learning tutorials.
- Play with new tools in cluster management, compilation, virtual world generation, or coding paradigms.
- Learn how to do research on bite-sized problems.
- Read about new developments in seemingly unrelated areas of AI.
We think Learning Day might be useful for other organizations, so we’d like to share how it started and works at OpenAI.
Backstory
We first tried out Learning Day on our Robotics team. Here’s how our Head of Robotics, Wojciech Zaremba (Woj), came up with the idea:
How it works
Learning Day happens each Thursday. Woj wrote the following guidelines for the Robotics team, but we’ve adapted these principles across each team that has adopted Learning Day:
To keep people accountable, we ask everyone to post in Slack what they learned that day.
What we learn on Learning Day
The following are examples of what people learn on a single Learning Day.
Deep learning reading
- “ Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules (opens in a new window) ”
- “ Learning Domain Randomization Distributions for Transfer of Locomotion Policies (opens in a new window) ”
- “ Neural Graph Evolution: Towards Efficient Automatic Robot Design Learning to Learn with Probabilistic Task Embeddings (opens in a new window) ”
- “ Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies (opens in a new window) ”
- “ Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables (opens in a new window) ”
- “ Does computer vision matter for action? (opens in a new window) ”
- “ WAIC, but Why? Generative Ensembles for Robust Anomaly Detection (opens in a new window) ”
- “ Weight Agnostic Neural Networks (opens in a new window) ”
- “ Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (opens in a new window) ”
- Deep unsupervised learning (opens in a new window)
- Deep RL Bootcamp (opens in a new window)
Deep learning coding
- Reptile and MAML (opens in a new window)
- Play with code in JAX (opens in a new window)
- Apply Sparse Transformers (opens in a new window) to vision tasks
- Implement LSTM and transformer from scratch; train them on Penn treebank
- Train a neural net to reproduce the behavior of a physical motor
Math and statistics
- Time Series Analysis (opens in a new window)
- The Book of Why: The New Science of Cause and Effect (opens in a new window)
Management
- Thanks for the Feedback: The Science and Art of Receiving Feedback Well (opens in a new window)
- Change Your Questions, Change Your Life: 10 Powerful Tools for Life and Work (opens in a new window)
Historical context on powerful technologies
- Dark Territory: The Secret History of Cyber War (opens in a new window)
- The Hacked World Order: How Nations Fight, Trade, Maneuver, and Manipulate in the Digital Age (opens in a new window)
- Technology Transfer to the USSR, 1928–1937 and 1966–1975: The Role of Western Technology in Soviet Economic Development (opens in a new window)
- The Turing Test: Verbal Behavior as the Hallmark of Intelligence (opens in a new window)
- The Information: A History, A Theory, A Flood (opens in a new window)
- Radical Markets: Uprooting Capitalism and Democracy for a Just Society (opens in a new window)
We also reimburse reasonable self-studying expenses such as books and tutors, used mostly to learn fundamentals in mathematics. These costs are very worthwhile investments!
How we sustain it
Learning Day’s impact comes from being rigorous about how people use it. It’s not a day for leisure, but instead a day for a specific kind of hard work. We see and try to counteract the following failure modes so that we can sustain it long term:
Learning Day could be used for work . Learning Day could turn into a normal working day because people may want to accomplish their main project faster (due to internal or external pressure). We prevent this by having Learning Day on the same day for every team. This creates positive peer pressure and encourages everyone to take advantage of Learning Day.
Learning Day could expand in scope to non–Learning Days . We actually haven’t yet observed this happen. Based on what we’ve seen with other organizations, we think this would most likely indicate that the person isn’t excited enough about their main project, and would be a sign to their manager that the person should switch teams or projects.
Learning Day could be used for leisure . Our solution is for every team member to share their progress on Slack via Geekbot (opens in a new window) . This keeps the excitement high and provides an accountability mechanism.
Learning Day beyond Robotics
We’ve recently expanded Learning Day from a subset of our technical teams to the entire company. It’s become a cultural staple—on our most recent internal survey, Learning Day was the aspect of our culture that people talked about the most. We’re excited to see its impact as we continue to evolve and support Learning Day in the future.
- Events
- Culture & Careers
- 2019
Author
Related articles
Company Jun 28, 2023
Company Dec 23, 2022
Company Dec 8, 2022