To ensure that Alexa Prize contestants can concentrate on dialogue systems — the core technology of socialbots — Amazon scientists and engineers built a set of machine learning modules that handle fundamental conversational tasks and a development environment that lets contestants easily mix and match existing modules with those of their own design.Read More
Machine learning can boost the value of wind energy
Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged. However, the variable nature of wind itself makes it an unpredictable energy sourceless useful than one that can reliably deliver power at a set time.In search of a solution to this problem, last year, DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central United States. These wind farmspart of Googles global fleet of renewable energy projectscollectively generate as much electricity as is needed by a medium-sized city.Using a neural network trained on widely available weather forecasts and historical turbine data, we configured the DeepMind system to predict wind power output 36 hours ahead of actual generation. Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance. This is important, because energy sources that can be scheduled (i.e.Read More
Horovod Adds Support for PySpark and Apache MXNet and Additional Features for Faster Training
This article was originally published on The LF Deep Learning Foundation Blog.
Horovod, a distributed deep learning framework created by Uber, makes distributed deep learning fast and easy-to-use. Horovod improves the speed, scale, and resource allocation for …
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Uber Open Source: Catching Up with Fritz Obermeyer and Noah Goodman from the Pyro Team
Over the past several years, artificial intelligence (AI) has become an integral component of many enterprise tech stacks, facilitating faster, more efficient solutions for everything from self-driving vehicles to automated messaging platforms. On the AI spectrum, deep probabilistic programming, a …
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Modeling Censored Time-to-Event Data Using Pyro, an Open Source Probabilistic Programming Language
Time-to-event modeling is critical to better understanding various dimensions of the user experience. By leveraging censored time-to-event data (data involving time intervals where some of those time intervals may extend beyond when data is analyzed), companies can gain insights on …
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First Uber Science Symposium: Discussing the Next Generation of RL, NLP, ConvAI, and DL
At Uber, hundreds of data scientists, economists, AI researchers and engineers, product analysts, behavioral scientists, and other practitioners leverage scientific methods to solve challenges on our platform. From modeling and experimentation to data analysis, algorithm development, and fundamental research, the …
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Introducing Ludwig, a Code-Free Deep Learning Toolbox
Over the last decade, deep learning models have proven highly effective at performing a wide variety of machine learning tasks in vision, speech, and language. At Uber we are using these models for a variety of tasks, including customer support…
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Why Alexa won’t wake up when she hears her name in Amazon’s Super Bowl ad
This Sunday’s Super Bowl between the New England Patriots and the Los Angeles Rams is expected to draw more than 100 million viewers, some of whom will have Alexa-enabled devices within range of their TV speakers. When Amazon’s new Alexa ad airs, and Forest Whitaker asks his Alexa-enabled electric toothbrush to play his podcast, how will we prevent viewers’ devices from mistakenly waking up?Read More