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Episodic Curiosity through Reachability: Observations are added to memory, reward is computed based on how far the current observation is from the most similar observation in memory. The agent receives more reward for seeing observations which are not yet represented in memory. Source: googleblog.com

Open source software pervades the work we do at Uber. On the infrastructure side, we have contributed projects like Jaeger, which lets engineers trace complex architectures, and M3, a metrics platform that works with Prometheus. For front-end development, we built RIBs, a cross-platform architecture for mobile apps, along with Fusion.js, a plugin-based web framework. In the rapidly advancing area of machine learning, we have open source tools such as Horovod, a distributed training framework, and Pyro, a deep probabilistic programming language written in Python.

As a company heavily invested in AI, Uber aims to leverage machine learning (ML) in product development and the day-to-day management of our business. In pursuit of this goal, our data scientists spend considerable amounts of time prototyping and validating powerful new types of ML models to solve Uber’s most challenging problems (e.g., NLP based smart reply systems, ticket assistance systems, fraud detection, and financial and marketplace forecasting). Once a model type is empirically validated to be best for the task, engineers work closely with data science teams to productionize and make it available for low latency serving at Uber-scale.