![[WiSDOM] Learning Values and Policies from Observation @UberEngineering](https://i.ytimg.com/vi/NZFnIu4U4Aw/maxresdefault.jpg)
Uploaded January 2020 | Updated September 2026, 2 weeks ago
On October 24, 2019, the WiSDOM (Women in Statistics, Data, Optimization, and Machine Learning) Employee Resource Group hosted the second annual Moving The World With Data meetup, welcoming hundreds of members of the Bay Area tech community to learn about some of our toughest, most interesting data science problems.
In this presentation, Ashley Edwards, a research scientist with Uber AI Labs, shares original work from the machine learning discipline of reinforcement learning. Using examples from the game Coin Run and other simple tasks, she demonstrates how an agent can be made to learn latent policies through observation. She also posed a question: in the future, can we expect robots to learn to act like humans by watching us move?
Learn more about the meetup: eng.uber.com/moving-the-world-with-data
On October 24, 2019, the WiSDOM (Women in Statistics, Data, Optimization, and Machine Learning) Employee Resource Group hosted the second annual Moving The World With Data meetup, welcoming hundreds of members of the Bay Area tech community to learn about some of our toughest, most interesting data science problems.
In this presentation, Ashley Edwards, a research scientist with Uber AI Labs, shares original work from the machine learning discipline of reinforcement learning. Using examples from the game Coin Run and other simple tasks, she demonstrates how an agent can be made to learn latent policies through observation. She also posed a question: in the future, can we expect robots to learn to act like humans by watching us move?
Learn more about the meetup: eng.uber.com/moving-the-world-with-data
![[WiSDOM] Learning Values and Policies from Observation](https://i.ytimg.com/vi/NZFnIu4U4Aw/mqdefault.jpg "[WiSDOM] Learning Values and Policies from Observation
On October 24, 2019, the WiSDOM (Women in Statistics, Data, Optimization, and Machine Learning) Employee Resource Group hosted the second annual Moving The World With Data meetup, welcoming hundreds of members of the Bay Area tech community to learn about some of our toughest, most interesting data science problems.
In this presentation, Ashley Edwards, a research scientist with Uber AI Labs, shares original work from the machine learning discipline of reinforcement learning. Using examples from the game Coin Run and other simple tasks, she demonstrates how an agent can be made to learn latent policies through observation. She also posed a question: in the future, can we expect robots to learn to act like humans by watching us move?
Learn more about the meetup: https://eng.uber.com/moving-the-world-with-data")
![[Uber Open Source] Ludwig: A Code-free Deep Learning Toolbox](https://i.ytimg.com/vi/Ns_6Ep7GAIM/mqdefault.jpg "[Uber Open Source] Ludwig: A Code-free Deep Learning Toolbox
During this April 2019 meetup in San Francisco, Uber research scientist, Piero Molino introduces Ludwig, a deep learning toolbox that lets people without a machine learning background train prediction models without the need to write code. Ludwig is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures.")
![[Visualization Nights] Designing Informational Analytics - JD Vogt & Raymon Sutedjo-The](https://i.ytimg.com/vi/Ntj5Cig68jE/mqdefault.jpg "[Visualization Nights] Designing Informational Analytics - JD Vogt & Raymon Sutedjo-The
Creating analytics apps and dashboards for business is more than picking the right chart. Its about understanding a users purpose and goals. During our June 2018 Visualization Night, JD Vogt and Raymon Sutedjo-The discuss how the Salesforce Analytics Design team approaches their work and the choices they make when creating analytics experiences, ranging from mapping out users path of inquiry to choosing the right charts and colors.")
![[Uber Open Summit 2018] Using Service Workers for Performance](https://i.ytimg.com/vi/OKUKUMxqlKk/mqdefault.jpg "[Uber Open Summit 2018] Using Service Workers for Performance
Service Workers are best known for allowing web applications to work offline, but they can also make your online pages load a lot faster. During Uber Open Summit 2018, engineer Angus Croll presents an overview of Service Workers and demonstrate why theyre your friend if you care about performance.")
![[Uber Open Summit 2018] Using Presto @ Uber](https://i.ytimg.com/vi/OXcIgd-0leE/mqdefault.jpg "[Uber Open Summit 2018] Using Presto @ Uber
During this Uber Open Summit 2018 tech talk, engineer Zhongting Hu discusses how we leverage Presto in our robust Big Data Platform.
Learn more about Uber Open Source: https://uber.github.io/")
![[Destination:Web] Building a Great Developer Experience: The Road to Parcel Version 2](https://i.ytimg.com/vi/Osuwky6rRiA/mqdefault.jpg "[Destination:Web] Building a Great Developer Experience: The Road to Parcel Version 2
In this tech talk delivered during a November 2019 Destination:Web Meetup, Adobe engineer Devon Govett discusses the origin of Parcel, a zero configuration web application bundler, how it distinguishes itself from similar tools, and whats in store for Parcel version 2.")
