Learning Day

Learning Day | OpenAI

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

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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:

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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:

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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”
• “ Learning Domain Randomization Distributions for Transfer of Locomotion Policies⁠”
• “ Neural Graph Evolution: Towards Efficient Automatic Robot DesignLearning to Learn with Probabilistic Task Embeddings⁠”
• “ Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies⁠”
• “ Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables⁠”
• “ Does computer vision matter for action?⁠”
• “ WAIC, but Why? Generative Ensembles for Robust Anomaly Detection⁠”
• “ Weight Agnostic Neural Networks⁠”
• “ Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations⁠”
• Deep unsupervised learning⁠
• Deep RL Bootcamp⁠

Deep learning coding

• Reptile⁠ and MAML⁠
• Play with code in JAX⁠
• Apply Sparse Transformers⁠ 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
• The Book of Why: The New Science of Cause and Effect⁠

Management

• Thanks for the Feedback: The Science and Art of Receiving Feedback Well⁠
• Change Your Questions, Change Your Life: 10 Powerful Tools for Life and Work⁠

Historical context on powerful technologies

• Dark Territory: The Secret History of Cyber War⁠
• The Hacked World Order: How Nations Fight, Trade, Maneuver, and Manipulate in the Digital Age⁠
• Technology Transfer to the USSR, 1928–1937 and 1966–1975: The Role of Western Technology in Soviet Economic Development⁠
• The Turing Test: Verbal Behavior as the Hallmark of Intelligence⁠
• The Information: A History, A Theory, A Flood⁠
• Radical Markets: Uprooting Capitalism and Democracy for a Just Society⁠

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. 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.

Author

OpenAI

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