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Uber introduces Hypothesis GU Func, a new extension to Hypothesis, as an open source Python package for unit testing. Unit testing is an important part of modern, collaborative software development. Especially as the number of project contributors grows, rigorous unit test coverage helps monitor and enforce high quality. Having a good system in place to generate test cases is important to identify difficult edge cases in your code. We use NumPy and PyTorch for building many machine learning (ML) models at Uber AI.

The pyAudioProcessing library classifies audio into different categories and genres. At a high level, any machine learning problem can be divided into three types of tasks: data tasks (data collection, data cleaning, and feature formation), training (building machine learning models using data features), and evaluation (assessing the model). Features, defined as ‘individual measurable propert[ies] or characteristic[s] of a phenomenon being observed,’ are very useful because they help a machine understand the data and classify it into categories or predict a value.

DevOps continues its steady growth throughout 2019 and beyond. Here are some highly recommended tools for you and your team to check out and implement into DevOps strategies. Find out exactly what you need for your specific use cases and goals. Have you tested them out? Finding the best DevOps tool is something that will take some experimentation and testing. Like previous years, DevOps is predicted for healthy growth in 2019 and beyond.

In 1997 IBM’s Deep Blue software beat the World Chess Champion Garry Kasparov in a series of six matches. Since then, other programs have beaten human players in games ranging from Jeopardy to Go. Inspired by his loss, Kasparov decided in 2005 to test the success of Human+AI pairs in an online chess tournament.2 He found that the Human+AI team bested the solo human. More surprisingly, he also found that the Human+AI team bested the solo computer, even though the machine outperformed humans.

A Convolutional Neural Network (CNN) is a class of deep, feed-forward artificial neural networks most commonly applied to analyzing visual imagery. The architecture of these networks was loosely inspired by biological neurons that communicate with each other and generate outputs dependent on the inputs. Though work on CNNs started in the early 1980s, they only became popular with recent technology advancements and computational capabilities that allow the processing of large amounts of data and the training of sophisticated algorithms in a reasonable amount of time.

Uber’s services require real-world coordination between a wide range of customers, including driver-partners, riders, restaurants, and eaters. Accurately forecasting things like rider demand and ETAs enables this coordination, which makes our services work as seamlessly as possible. In an effort to constantly optimize our operations, serve our customers, and train our systems to perform better and better, we leverage machine learning (ML). In addition, we make many of our ML tools open source, sharing them with the community to advance the state of the art.

Neural networks (NNs) have become prolific over the last decade and now power machine learning across the industry. At Uber, we use NNs for a variety of purposes, including detecting and predicting object motion for self-driving vehicles, responding more quickly to customers, and building better maps. While many NNs perform quite well at their tasks, networks are fundamentally complex systems, and their training and operation is still poorly understood.

Our waking and sleeping lives are punctuated by fragments of recalled memories: a sudden connection in the shower between seemingly disparate thoughts, or an ill-fated choice decades ago that haunts us as we struggle to fall asleep. By measuring memory retrieval directly in the brain, neuroscientists have noticed something remarkable: spontaneous recollections, measured directly in the brain, often occur as very fast sequences of multiple memories. These so-called ‘replay’ sequences play out in a fraction of a second–so fast that we’re not necessarily aware of the sequence.

A team of researchers recently presented their paper on KiloGram, a new algorithm for managing large n-grams in files, to improve machine-learning detection of malware. The new algorithm is 60x faster than previous methods and can handle n-grams for n=1024 or higher. The large values of n have additional application for interpretable malware analysis and signature generation. Source: infoq.com

To unlock the full potential of virtual reality (VR) and augmented reality (AR) experiences, the technology needs to work anywhere, adapting to the spaces where people live and how they move within those real-world environments. When we developed Oculus Quest, the first all-in-one, completely wire-free VR gaming system, we knew we needed positional tracking that was precise, accurate, and available in real time — within the confines of a standalone headset, meaning it had to be compact and energy efficient.