Worker activists at Google held a "town hall" on Friday where they alleged that the company regularly retaliates against employees who speak out about workplace problems and announced plans for a "company-wide day of action" on 1 May. The meeting, livestreamed for Google employees in offices around the world, was announced after two of the organizers of the November 2018 global walkout circulated a letter internally alleging they were being punished for their activism. The two employees, Meredith Whittaker and Claire Stapleton, provided further details of their cases during the Friday event. Their statements, along with anonymous reports of retaliation of 11 other Google employees, were published in internal documents seen by the Guardian. "I didn't walk out because I'm against Google, I walked out because I'm for it – because I wanted to make it better," Stapleton said in her written statement.

Uber has unveiled the terms of a hotly anticipated stock market float which it hopes will value the ride-hailing service at more than $91bn (£70bn). While the target is $10bn less than some bankers suggested the 10-year-old firm might be worth, the valuation is more than double the value of the 116-year-old carmaker Ford and would be the largest float by a US tech company since Facebook's in 2012. Its Wall Street debut will gauge investors' excitement about the prospects of a company that has expanded rapidly from taxi services into food delivery and is now investing billions in developing driverless cars. If it hits the mark, Uber will raise around $9bn in new funds and some early investors will make big profits. Despite the scale of ts ambition, Uber lost $1.8bn last year even while its revenues surged by more than 40% to $11.3bn.

Fans of "The Walking Dead" will likely relish "Days Gone," the new video game thriller out Friday for Sony PlayStation 4. The post-apocalyptic premise is similar: Your character has survived a global pandemic, while millions have died or turned into ferocious predatory humanoids. If you've only seen the game depicted in TV commercials, at first glance you might be excused for considering these creatures as cousins of those "Walking Dead" zombies. But no, these beasts are transformed humans called "Freakers." "Days Gone" creative director and writer John Garvin explains: "What we have done here is actually create something that is new. They are not really mutants, they are certainly not demons or aliens or robots. They are not zombies either. They are their own thing."

That's because Tesla has created "the best chip in the world." Imagine if your car could get new features or even become faster or get to farther on a fill-up just while sitting parked outside. To the delight of Tesla owners everywhere, the electric car company continually offers over-the-air software updates to the cars they already have, making it feel as if they are walking out to a brand new vehicle, instead of having to wait a year or so for a new, improved model. And now, CEO Elon Musk is talking about creating a fleet of Uber-like "robotaxis" with just such an upgrade. In March, with the introduction of the base $35,000 Model 3, a Tesla update made all existing Model 3s faster, practically overnight.

You might say that when it comes to delivery, Amazon is flooding the zone. With Walmart and Target nipping at its heels, the e-commerce giant is determined to win the delivery wars by dropping off packages wherever, and whenever, a shopper might want to receive them. For the first time, this year, Amazon delivered items to Coachella. On a business trip but didn't pack your tie? Hilton Hotels are among the locations where you can have one shipped to an Amazon locker. In Snohomish County, Washington, a robot may bring packages to your door.

Women belonging to the socially disadvantaged caste-groups in India have historically been engaged in labour-intensive, blue-collar work. We study whether there has been any change in the ability to predict a woman's work-status and work-type based on her caste by interpreting machine learning models using feature attribution. We find that caste is now a less important determinant of work for the younger generation of women compared to the older generation. Moreover, younger women from disadvantaged castes are now more likely to be working in white-collar jobs.

With the rapid development of knowledge bases(KBs),question answering(QA)based on KBs has become a hot research issue. In this paper,we propose two frameworks(i.e.,pipeline framework,an end-to-end framework)to focus answering single-relation factoid question. In both of two frameworks,we study the effect of context information on the quality of QA,such as the entity's notable type,out-degree. In the end-to-end framework,we combine char-level encoding and self-attention mechanisms,using weight sharing and multi-task strategies to enhance the accuracy of QA. Experimental results show that context information can get better results of simple QA whether it is the pipeline framework or the end-to-end framework. In addition,we find that the end-to-end framework achieves results competitive with state-of-the-art approaches in terms of accuracy and take much shorter time than them.

Feature space can change or evolve when learning with streaming data. Several recent works have studied feature evolvable learning. They usually assume that features would not vanish or appear in an arbitrary way. For example, when knowing the battery lifespan, old features and new features represented by data gathered by sensors will disappear and emerge at the same time along with the sensors exchanging simultaneously. However, different sensors would have different lifespans, and thus the feature evolution can be unpredictable. In this paper, we propose a novel paradigm: Prediction with Unpredictable Feature Evolution (PUFE). We first complete the unpredictable overlapping period into an organized matrix and give a theoretical bound on the least number of observed entries. Then we learn the mapping from the completed matrix to recover the data from old feature space when observing the data from new feature space. With predictions on the recovered data, our model can make use of the advantage of old feature space and is always comparable with any combinations of the predictions on the current instance.

MLaaS (ML-as-a-Service) offerings by cloud computing platforms are becoming increasingly popular these days. Pre-trained machine learning models are deployed on the cloud to support prediction based applications and services. For achieving higher throughput, incoming requests are served by running multiple replicas of the model on different machines concurrently. Incidence of straggler nodes in distributed inference is a significant concern since it can increase inference latency, violate SLOs of the service. In this paper, we propose a novel coded inference model to deal with stragglers in distributed image classification. We propose modified single shot object detection models, Collage-CNN models, to provide necessary resilience efficiently. A Collage-CNN model takes collage images formed by combining multiple images as its input and performs multi-image classification in one shot. We generate custom training collages using images from standard image classification datasets and train the model to achieve high classification accuracy. Deploying the Collage-CNN models in the cloud, we demonstrate that the 99th percentile latency can be reduced by 1.45X to 2.46X compared to replication based approaches and without compromising prediction accuracy.

In this letter, we address the problem of controlling energy storage systems (ESSs) for arbitrage in real-time electricity markets under price uncertainty. We first formulate this problem as a Markov decision process, and then develop a deep reinforcement learning based algorithm to learn a stochastic control policy that maps a set of available information processed by a recurrent neural network to ESSs' charging/discharging actions. Finally, we verify the effectiveness of our algorithm using real-time electricity prices from PJM.