San Francisco quietly amends its municipal surveillance legislation to permit for Apple's Face ID, although the ban on facial recognition nonetheless applies. After San Francisco in Might positioned new controls, together with a ban on facial recognition, on municipal surveillance, metropolis workers started taking inventory of what know-how businesses already owned. They shortly realized that the town owned numerous facial recognition know-how--a lot of it in employees' pockets. Metropolis-issued iPhones geared up with Apple's signature unlock characteristic, Face ID, had been now unlawful--even when the characteristic was turned off, says Lee Hepner, an aide to supervisor Aaron Peskin, the member of the native Board of Supervisors who spearheaded the ban. Across the similar time, police division staffers scurried to disable a facial recognition system for looking out mug pictures that was unknown to the general public or Peskin's workplace.

ABOUT THE AUTHOR Usman Sheikh Founder and CEO xiQ, Inc.Usman Sheikh is founder and CEO of xiQ, Inc. xiQ has been developed on three principles: Leverage AI to reimagine the buyers journey and customer engagement Use of mobile technology to provide ubiquitous access to business critical information Design thinking to develop user-centric experiences Prior to founding xiQ, Usman was a Vice President with SAP, SE where he had first hand experience with ABM and B2B Sales. Usman has worked in over 40 countries and lived in Singapore, Germany and the United States. Currently he resides in the Silicon Valley, San Francisco Bay Area.

Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target. While several deep learning models have been proposed for multi-step prediction, they typically comprise black-box models which do not account for the full range of inputs present in common scenarios. In this paper, we introduce the Temporal Fusion Transformer (TFT) -- a novel attention-based architecture which combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. To learn temporal relationships at different scales, the TFT utilizes recurrent layers for local processing and interpretable self-attention layers for learning long-term dependencies. The TFT also uses specialized components for the judicious selection of relevant features and a series of gating layers to suppress unnecessary components, enabling high performance in a wide range of regimes. On a variety of real-world datasets, we demonstrate significant performance improvements over existing benchmarks, and showcase three practical interpretability use-cases of TFT.

China is planning to upgrade its naval power with unmanned AI submarines that aim to provide an edge over the fleets of their global counterparts. A report by the South China Post on Sunday revealed Beijing's plans to build the automated subs by the early 2020s in response to unmanned weapons being developed in the US. The subs will be able to patrol areas in the South China Sea and Pacific Ocean that are home to disputed military bases. While the expected cost of the submarines has not been disclosed, they're likely to be cheaper than conventional submarines as they do not require life-supporting apparatus for humans. However, without a human crew, they'll also need to be resilient enough to be at sea without onboard repairs possible. The XLUUVs (Extra-Large Unmanned Underwater Vehicles) are much bigger than current underwater vehicles, will be able to dock as any other conventional submarine, and will carry a large amount of weaponry and equipment.

Wired editor Maria Streshinsky speaks to computer and data science experts Kai-Fu Lee and Fei-Fei Li. We're told constantly that artificial Intelligence (AI) is ever-rising in its ubiquity, seeping into every industry, finding its place in all aspects of the business-- enabling us to work in different ways; in some cases, threatening to take over our roles entirely. Stats such as recruitment firm Robert Walters', which predicts AI will give rise to 133 million new jobs across the globe in the future, can sound vague and far off in a distant future, while things probably haven't seemed to have changed much at our desks. But rest assured, hype aside, the'age of AI' is drawing closer, and the evidence lies in businesses' eagerness to invest in the talent to make it happen. The AI specialist now represents the fastest-growing role in the United States over the last four years.

That role, A.I. specialist, is the fastest growing U.S. job in terms of number of hires, at least according to LinkedIn, which published its annual emerging jobs report on Tuesday. Hirings for A.I. specialists on the career networking service have grown 74% annually over the past four years, LinkedIn said. But it didn't reveal how many jobs that represents, only that demand for that job role is growing faster than other emerging jobs. What's noteworthy about this year's survey is that last year's top job role, blockchain developer, is absent from the latest list. It highlights how the recent craze over cryptocurrencies and blockchain created a brief demand for blockchain-related jobs, but as the hype died down, so too did demand for people with blockchain skills.

It's never a bad time to be an engineer--or to have people skills. LinkedIn's third annual US emerging jobs report has identified the 15 fastest-growing jobs, as well as the skills and cities most associated with them. This year the company found that the number of artificial intelligence and data science roles continue to expand across nearly every industry. For the first time, robotics has made an appearance on the list, and at least five roles in the ranking include the word "engineer" in the title. But it's not just high-tech roles that have seen a lot more hiring action in the past five years, which is how far back LinkedIn looks to measure the emergence of roles based on user profile data and hiring growth trends.

Multivariate time series forecasting is an important yet challenging problem in machine learning. Most existing approaches only forecast the series value of one future moment, ignoring the interactions between predictions of future moments with different temporal distance. Such a deficiency probably prevents the model from getting enough information about the future, thus limiting the forecasting accuracy. To address this problem, we propose Multi-Level Construal Neural Network (MLCNN), a novel multi-task deep learning framework. Inspired by the Construal Level Theory of psychology, this model aims to improve the predictive performance by fusing forecasting information (i.e., future visions) of different future time. We first use the Convolution Neural Network to extract multi-level abstract representations of the raw data for near and distant future predictions. We then model the interplay between multiple predictive tasks and fuse their future visions through a modified Encoder-Decoder architecture. Finally, we combine traditional Autoregression model with the neural network to solve the scale insensitive problem. Experiments on three real-world datasets show that our method achieves statistically significant improvements compared to the most state-of-the-art baseline methods, with average 4.59% reduction on RMSE metric and average 6.87% reduction on MAE metric.

SpaceX launched a 3-ton cargo payload to the International Space Station (ISS) on Thursday, which included barley grains for a beer experiment, mice for muscle-building research and a robot designed to show empathy. The Falcon 9 rocket carrying the recycled Dragon capsule filled with the goodies lifted off from Cape Canaveral, Fla., around 12:30 p.m. The capsule is expected to arrive at the station housing six astronauts -- three Americans, two Russians and one Italian -- on Sunday. SpaceX recovered the new booster on a barge just off the coast in the Atlantic several minutes following liftoff so that it could be reused. SpaceX employees in Southern California cheered when the booster landed, and again a few minutes later when the capsule reached orbit.

Prototype of the Force 12 Xplorer being tested near Victoria, British Columbia. It uses a rigid ... [ ] wingsail for propulsion. It's been a great year for Open Ocean Robotics, a British Columbia-based startup that makes solar-powered drones that can gather environmental data in real time and help address a multitude of issues. During 2019, Open Ocean Robotics won a most-promising startup award from the National Community for Angels, Incubators, and Accelerators; $100,000 in a Spring Impact Investor Challenge; and was a finalist in a New Ventures BC Competition, to name a few. So how do you follow that up for 2020?