After years of development, Alphabet's drone delivery service Wing is officially open for business. The company announced the beginning of service for residents of Christiansburg, Virginia, who will be able to order over-the-counter medication, snacks, and other small items and have them airlifted straight to their homes by a drone. Initially, Wing will deliver goods on behalf of three partner companies with FedEx, Walgreens, and Super Magnolia, a local Virginia grocery store chain. After years of preparation, Alphabet's drone delivery service Wing has officially begun operations in Christiansburg, Virginia The company made the announcement via a blog post on Medium and included a video showing how the delivery service will work. The FAA approved Alphabet's drone delivery program in March, and the company announced it's plans for'store to door' of more than 100 products in Virginia last September.

As of this afternoon, select residents of Christiansburg, Virginia can tap drones operated by Google parent company Alphabet's Wing for quick and easy deliveries of packages, over-the-counter medications, snacks, and gifts. The company today revealed that it's become the first to operate a commercial air delivery service directly to homes in the U.S., with the launch of a previously announced pilot involving FedEx Express, Walgreens, and local Virginia retailer Sugar Magnolia. From Walgreens, the first retail pharmacy to partner with Wing in the U.S., drone fleets will ferry over-the-counter medicines and other wellness items to folks' homes. And on the FedEx Express side, recipients living within designated Christiansburg zones who opt in will receive some shipments via drone, in customized boxes. Most orders within the four-mile radius of Wing's distribution facility are fulfilled within about 10 minutes, according to the company.

SINGAPORE, October 18th, 2019 – Asia/Pacific* spending on artificial intelligence (AI) systems will reach USD 6.2 billion in 2019, recording an increase of almost 54% when compared to 2018, according to the latest IDC Worldwide Semiannual Artificial Intelligence Systems Spending Guide. Evidently, as industries invest aggressively in projects that utilize AI software capabilities, IDC expects spending on AI systems will increase to USD 21.4 billion by 2023 with a compound annual growth rate (CAGR) of 39.6% over the 2018-23 forecast period. From providing chat bots for better customer service to improve the efficiency of operations and tasks for their business models, industries like Banking, Retail and professional services are spending in this technology at scale says," Ritika Srivastava, Associate Market Analyst at IDC Asia/Pacific. In 2019, Asia/Pacific* spending on AI systems will be led by the Banking industry with 10.7% share of the total, followed by retail with a 10.2% share.

FILE - In this June 4, 1942 file photo provided by the U.S. Navy shows the USS Yorktown listing heavily to port after being struck by Japanese bombers and torpedo planes in the Battle of Midway. Researchers scouring the world's oceans for sunken World War II ships are honing in on debris fields deep in the Pacific.(AP MIDWAY ATOLL, Northwestern Hawaiian Islands (AP) -- Deep-sea explorers scouring the world's oceans for sunken World War II ships are focusing on debris fields deep in the Pacific, in an area where one of the most decisive battles of the time took place. Hundreds of miles off Midway Atoll, nearly halfway between the United States and Japan, a research vessel is launching underwater robots miles into the abyss to look for warships from the famed Battle of Midway. Weeks of grid searches around the Northwestern Hawaiian Islands have already led the crew of the Petrel to one sunken warship, the Japanese ship the Kaga.

At its core, technology exists to make our lives easier. Thanks to artificial intelligence, our tools have gotten smarter, and we're more productive as a result. According to a study released earlier today, workers around the world not only recognize AI's importance in the modern workplace – they embrace it. Conducted over the summer in partnership between Oracle and Future Workspace, the second annual AI at Work study asked 8,370 employees, managers and HR leaders from 10 countries about AI and its place in their work. Researchers found that AI is rapidly changing not only how we conduct business, but the very relationship between people and the tech they use every day.

The results of data mining endeavors are majorly driven by data quality. Throughout these deployments, serious show-stopper problems are still unresolved, such as: data collection ambiguities, data imbalance, hidden biases in data, the lack of domain information, and data incompleteness. This paper is based on the premise that context can aid in mitigating these issues. In a traditional data science lifecycle, context is not considered. Context-driven Data Science Lifecycle (C-DSL); the main contribution of this paper, is developed to address these challenges. Two case studies (using data-sets from sports events) are developed to test C-DSL. Results from both case studies are evaluated using common data mining metrics such as: coefficient of determination (R2 value) and confusion matrices. The work presented in this paper aims to re-define the lifecycle and introduce tangible improvements to its outcomes.

The usability and practicality of any machine learning (ML) applications are largely influenced by two critical but hard-to-attain factors: low latency and low cost. Unfortunately, achieving low latency and low cost is very challenging when ML depends on real-world data that are highly distributed and rapidly growing (e.g., data collected by mobile phones and video cameras all over the world). Such real-world data pose many challenges in communication and computation. For example, when training data are distributed across data centers that span multiple continents, communication among data centers can easily overwhelm the limited wide-area network bandwidth, leading to prohibitively high latency and high cost. In this dissertation, we demonstrate that the latency and cost of ML on highly-distributed and rapidly-growing data can be improved by one to two orders of magnitude by designing ML systems that exploit the characteristics of ML algorithms, ML model structures, and ML training/serving data. We support this thesis statement with three contributions. First, we design a system that provides both low-latency and low-cost ML serving (inferencing) over large-scale and continuously-growing datasets, such as videos. Second, we build a system that makes ML training over geo-distributed datasets as fast as training within a single data center. Third, we present a first detailed study and a system-level solution on a fundamental and largely overlooked problem: ML training over non-IID (i.e., not independent and identically distributed) data partitions (e.g., facial images collected by cameras varies according to the demographics of each camera's location).

Scheduling of personnel in a hospital environment is vital to improving the service provided to patients and balancing the workload assigned to clinicians. Many approaches have been tried and successfully applied to generate efficient schedules in such settings. However, due to the computational complexity of the scheduling problem in general, most approaches resort to heuristics to find a non-optimal solution in a reasonable amount of time. We designed an integer linear programming formulation to find an optimal schedule in a clinical division of a hospital. Our formulation mitigates issues related to computational complexity by minimizing the set of constraints, yet retains sufficient flexibility so that it can be adapted to a variety of clinical divisions. We then conducted a case study for our approach using data from the Infectious Diseases division at St. Michael's Hospital in Toronto, Canada. We analyzed and compared the results of our approach to manually-created schedules at the hospital, and found improved adherence to departmental constraints and clinician preferences. We used simulated data to examine the sensitivity of the runtime of our linear program for various parameters and observed reassuring results, signifying the practicality and generalizability of our approach in different real-world scenarios.

Newspapers are a popular form of written discourse, read by many people, thanks to the novelty of the information provided by the news content in it. A headline is the most widely read part of any newspaper due to its ap - pearance in a bigger font and sometimes in colour print. In this paper, we sug - gest and implement a method for computing inferences from English news headlines, excluding the information from the context in which the headlines appear. This method attempts to generate the possible assumptions a reader formulates in mind upon reading a fresh headline. The generated inferences could be useful for assessing the impact of the news headline on readers includ - ing children. The understandability of the current state of social affairs depends greatly on the assimilation of the headlines. As the inferences that are indepen - dent of the context depend mainly on the syntax of the headline, dependency trees of headlines are used in this approach, to find the syntactical structure of the headlines and to compute inferences out of them.

Context modeling has a pivotal role in open domain conversation. Existing works either use heuristic methods or jointly learn context modeling and response generation with an encoder-decoder framework. This paper proposes an explicit context rewriting method, which rewrites the last utterance by considering context history. We leverage pseudo-parallel data and elaborate a context rewriting network, which is built upon the CopyNet with the reinforcement learning method. The rewritten utterance is beneficial to candidate retrieval, explainable context modeling, as well as enabling to employ a single-turn framework to the multi-turn scenario. The empirical results show that our model outperforms baselines in terms of the rewriting quality, the multi-turn response generation, and the end-to-end retrieval-based chatbots.