2287 results for "earn" across all locations
Applying Customer Feedback: How NLP & Deep Learning Improve Uber’s Maps
To improve our maps, Uber Engineering analyzes customer support tickets with natural language processing and deep learning to identify and correct inaccurate map data.
Transforming Financial Forecasting with Data Science and Machine Learning at Uber
Uber developed its own financial planning software, relying on data science and machine learning, to deliver on-demand forecasting and optimize strategic and operations decisions.
Up: Portable Microservices Ready for the Cloud
Learn how Uber built Up, our stateless platform that runs containers anywhere and automatically moves them between on-prem and cloud providers.
Data Science at Scale: A Conversation with Uber’s Fran Bell
We spoke to Data Science Director Fran Bell about machine learning at Uber and what she finds most challenging—and rewarding—about her work.
Uber and bp charge partner to help driver-partners and delivery people go electric
Uber and bp charge partner to help drivers and delivery people go electric. bp charge is providing all driver-partners and delivery people in New Zealand with exclusive discounts on EV charging.
AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
J. Clune
Perhaps the most ambitious scientific quest in human history is the creation of general artificial intelligence, which roughly means AI that is as smart or smarter than humans. The dominant approach in the machine learning community is to attempt to discover each of the pieces required for intelligence, with the implicit assumption that some future group will complete the Herculean task of figuring out how to combine all of those pieces into a complex thinking machine. […] [PDF]
2016
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Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
T. Miconi, A. Rawal, J. Clune, K. Stanley
A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma’s Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, which is the dominant method to improve performance on hard-exploration domains. To address this shortfall, we introduce a new algorithm called Go-Explore. […] [PDF]
International Conference on Learning Representations (ICLR), 2019
Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients
A. Edwards, Himanshu Sahni, R. Liu, J. Hung, A. Jain, R. Wang, A. Ecoffet, T. Miconi, C. Isbell, J. Yosinski
In this paper, we introduce a novel form of value function, Q(s,s′), that expresses the utility of transitioning from a state s to a neighboring state s′ and then acting optimally thereafter. In order to derive an optimal policy, we develop a forward dynamics model that learns to make next-state predictions that maximize this value. […] [PDF]
International Conference on Machine Learning (ICML), 2020
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