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Uber wants to get better at predicting customer demand

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Uber wants to use machine learning to predict when a surge of people will be out looking for rides. The intention is to get more cabs on the road before surge pricing would normally kick in. That way, drivers will be ready and waiting for riders when the surge happens -- and riders won't be stuck waiting around. Here's how Jeff Schneider, the engineering leader of Uber's Advanced Technology Center, put it during a recent data technology conference: "This idea is if you can predict that demand, you get that information out there -- and you get that supply there ready for the demand so the surge pricing never even has to happen," he said, according to NPR. Uber already does this to some extent, but Schneider says that Uber wants "to find those Tuesday nights when it's not even raining and for some reason there's demand."


Human-Centered Machine Learning – Google Design – Medium

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Machine learning (ML) is the science of helping computers discover patterns and relationships in data instead of being manually programmed. It's a powerful tool for creating personalized and dynamic experiences, and it's already driving everything from Netflix recommendations to autonomous cars. But as more and more experiences are built with ML, it's clear that UXers still have a lot to learn about how to make users feel in control of the technology, and not the other way round. As was the case with the mobile revolution, and the web before that, ML will cause us to rethink, restructure, displace, and consider new possibilities for virtually every experience we build. In the Google UX community, we've started an effort called "human-centered machine learning" (HCML) to help focus and guide that conversation.


Waymo Shares Autonomous Vehicle Dataset for Machine Learning

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Waymo, the self-driving technology company owned by Google's parent company, Alphabet, released a dataset containing sensor data collected by their autonomous vehicles during more than five hours of driving. The set contains high-resolution data from lidar and camera sensors collected in several urban and suburban environments in a wide variety of driving conditions, and includes labels for vehicles, pedestrians, cyclists, and signage. The Waymo team announced the release of the Waymo Open Dataset in a blog post, describing it as "one of the largest, richest, and most diverse self-driving datasets ever released for research." The data was collected by Waymo's vehicles operating in the USA in Phoenix, AZ, Kirkland, WA, Mountain View, CA and San Francisco, CA, at various times of day and night, and in good and bad weather. The dataset consists of 1,000 segments of 20 seconds each, collected at 10Hz (i.e., 200,000 frames) which contain: Waymo also released a Google Colab notebook containing tutorials and a GitHub repository containing TensorFlow helper-code for building models.


How Data Managers are Steering Us Toward a Better and Safer Future on the Roads

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Autonomous vehicles are on the rise to combat the country's motor vehicle fatalities. This article by Red Hat's Pete Brey takes a dive on how machine learning, artificial intelligence, and deep learning work together to achieve this goal. Houston, we have a problem. So does Los Angeles, Atlanta, New York, D.C, Boston, and all cities, towns, and counties throughout the United States. That problem is motor vehicle fatalities.


Artificial Intelligence: What Is Reinforcement Learning?

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Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. In this article, I want to provide a simple guide that explains reinforcement learning and give you some practical examples of how it is used today. At the core of reinforcement learning is the concept that the optimal behavior or action is reinforced by a positive reward. Similar to toddlers learning how to walk who adjust actions based on the outcomes they experience such as taking a smaller step if the previous broad step made them fall, machines and software agents use reinforcement learning algorithms to determine the ideal behavior based upon feedback from the environment. Depending on the complexity of the problem, reinforcement learning algorithms can keep adapting to the environment over time if necessary in order to maximize the reward in the long-term.