Construction on a brand new Universal Studios in Beijing is underway! Estimated at about 300 acres large, the theme park and neighboring resort will include attractions from other Universal theme parks as well as new attractions that reflects China's cultural heritage. Universal Studio's first theme park in China also aims to be a "smart city" in its development and management, and it will be constructed with the help of advanced technologies such as Building Information Modelling (BIM), augmented and virtual reality (AR/VR), and artificial intelligence. In fact, Autodesk and BSH Investment recently signed a Memorandum of Understanding (MOU) to explore the use of VR, human and machine interaction and Autodesk's Forge platform for the development of the world-class tourist destination. The use of advanced technologies in the construction of Universal Studios Beijing is aligned with China's 13th Five-Year Plan.

Watson won't be wearing anything fancy to the Grammys this weekend, but that's not going to keep it from judging everyone else's outfit. For the 60th anniversary of the music awards, the Recording Academy is partnering with IBM to bring its artificial intelligence to the red carpet. On Sunday night, IBM will deploy its AI platform to analyze videos and photos of nominees and attendees as they arrive at the ceremony in New York. In addition to identifying each person, Watson will be able to understand styles, learn about this year's fashion trends and compare them to those of previous years. People will then curate those findings, along with a selection of the many photos and videos taken by photographers, and upload them to the Grammys website for fans to learn more about their favorite musicians along with those honored decades ago.

We consider the task of interpolating and forecasting a time series in the presence of noise and missing data. As the main contribution of this work, we introduce an algorithm that transforms the observed time series into a matrix, utilizes singular value thresholding to simultaneously recover missing values and de-noise observed entries, and performs linear regression to make predictions. We argue that this method provides meaningful imputation and forecasting for a large class of models: finite sum of harmonics (which approximate stationary processes), non-stationary sublinear trends, Linear Time-Invariant (LTI) systems, and their additive mixtures. In general, our algorithm recovers the hidden state of dynamics based on its noisy observations, like that of a Hidden Markov Model (HMM), provided the dynamics obey the above stated models. We demonstrate on synthetic and real-world datasets that our algorithm outperforms standard software packages not only in the presence of significantly missing data with high levels of noise, but also when the packages are given the underlying model while our algorithm remains oblivious.

In the Central American country of Honduras, a political story has been unfolding which deserves more coverage than it has been getting. Close to 40 people have been killed and more than 2,000 arrested, following the contested re-election of President Juan Orlando Hernandez to a second term in office. With 54 percent of the votes counted, the trend was a clear win for left-wing opposition candidate, Salvador Nasralla. But then the computer system mysteriously broke down. When it finally came back online a full day later, the vote count had been turned upside down: the right-wing incumbent, Juan Orlando Hernandez, was suddenly ahead.

The first thing to understand about the fatal incident involving a pedestrian and self-driving Uber vehicle is the lay of the land, literally. Primary avenues in metropolitan Phoenix are very wide. The typical right of way for a major "arterial" is 140 feet. This is a metro engineered for driving, moving cars quickly and efficiently. Thousands of shade trees were felled over the decades to widen streets.

Reprinted with permission from HPCWire. Petaflop per second deep learning training performance on the Cori supercomputer at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC) has given climate scientists the ability to use machine learning to identify extreme weather events in huge climate simulation datasets. Predictive accuracies ranging from 89.4% to as high as 99.1% show that trained deep learning neural networks (DNNs) can identify weather fronts, tropical cyclones, and long narrow air flows that transport water vapor from the tropics called atmospheric rivers (Figure 1). Figure 1: Relation between ground truth (green boxes) and classification plus regression results (red boxes) of the DNN trained to recognize atmospheric phenomena. The strong relationship between ground truth and the neural network prediction can be seen in the classification plus regression results reported by Berkeley Lab climate scientist Michael Wehner at the Intel Developer Conference held during SC17 last November in Denver, Colorado. Supercomputers like NERSC's Cori system provide scientists with an extraordinary tool to model climate change significantly faster and far more accurately than was possible on previous generation supercomputers.

Apple Inc. AAPL 0.78% launched its music-streaming service in 2015 a year after buying Beats Electronics LLC. Its debut stumbled over user interface and engineering problems, but the service was revamped within a year and quickly became the No. 2 on-demand service. Apple's iTunes, where customers pay to download individual songs or albums and own them permanently, is separate but accessible through the platform. Apple Music has benefited from its integration with Apple devices, from iPhones and MacBooks to Apple Watches and HomePod voice-activated speakers, which sync easily with Apple Music but less so with Spotify or other services. Apple Chief Executive Tim Cook has said streaming isn't a moneymaking business but has emphasized the importance of providing music and supporting artists.

Technology has always played a role in inspiring musicians in new and creative ways. The guitar amp gave rock musicians a new palette of sounds to play with in the form of feedback and distortion. And the sounds generated by synths helped shape the sound of electronic music. How might they play a role in creating new tools and possibilities for a musician's creative process? Magenta, a research project within Google, is currently exploring answers to these questions. Building upon past research in the field of machine learning and music, last year Magenta released NSynth (Neural Synthesizer).

In the 67 years since Alan Turing proposed his Imitation Game - the infamous'Turing test' for artificial intelligence (AI) - people have been confused over the very purpose of AI itself. At issue: whether the point of AI is to simulate human behavior so seamlessly that it can fool people into thinking they are actually interacting with a human being, rather than a piece of software. Such deception was never the point of Turing's exercise, however. Rather, he realized that there was no way to define true intelligence, and thus no way to test for it. So he came up with the game as a substitute - something people could theoretically test for.

ANYmal carries an onboard microphone with which music can be perceived. The beat of the music is analyzed and a suitable sequence of dance motions is choreographed. The desired and real motion trajectories are compared such that the delay between music and motion can be minimized. This work has been conducted as part of ANYmal Research, a community to advance legged robotics.