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New Google Maps feature will show routes to nearest public transport

Daily Mail - Science & tech

Google Maps is working on a new feature that will show you how to reach the nearest public transport connection, according to new leaked screenshots. The new Maps filter will let users choose what mode of transportation they will be using at the very beginning of their daily commute, the screenshots show. Once rolled out, the feature will allow commuters to work out their preferred route to various transport connections, such as the train station, when they return to the workplace after the coronavirus pandemic. The screenshots also reveal an option to get more accurate Uber fares using data from Google Maps and a slightly new design for the Maps interface. 'Google Maps is working on route options with "Connections to Public Transit", such as car and transit, bicycle and transit, auto rickshaw, ride service [and] motorcycle and transit,' said Jane Wong, a Hong Kong-based hacker, tech blogger and software engineer, who leaked the screenshots.


Event Detection in Noisy Streaming Data with Combination of Corroborative and Probabilistic Sources

arXiv.org Machine Learning

--Global physical event detection has traditionally relied on dense coverage of physical sensors around the world; while this is an expensive undertaking, there have not been alternatives until recently. The ubiquity of social networks and human sensors in the field provides a tremendous amount of real-time, live data about true physical events from around the world. However, while such human sensor data have been exploited for retrospective large-scale event detection, such as hurricanes or earthquakes, they has been limited to no success in exploiting this rich resource for general physical event detection. Prior implementation approaches have suffered from the concept drift phenomenon, where real-world data exhibits constant, unknown, unbounded changes in its data distribution, making static machine learning models ineffective in the long term. We propose and implement an end-to-end collaborative drift adaptive system that integrates corroborative and probabilistic sources to deliver real-time predictions. Furthermore, out system is adaptive to concept drift and performs automated continuous learning to maintain high performance. We demonstrate our approach in a real-time demo available online for landslide disaster detection, with extensibility to other real-world physical events such as flooding, wildfires, hurricanes, and earthquakes. Physical event detection, such as extreme weather events or traffic accidents have long been the domain of static event processors operating on numeric sensor data or human actors manually identifying event types. However, the emergence of big data and associated data processing and analytics tools and systems have led to several applications in large-scale event and trend detection in the streaming domain [1]-[7]. However, it is important to note that many of these works are a form of retrospective analysis, as opposed to true real-time event detection, since they perform analyses on cleaned and processed data within a short-time frame in the past, with the assumption that their approaches are sustainable and will continue to function over time.


A 20-Year Community Roadmap for Artificial Intelligence Research in the US

arXiv.org Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.


Real-Time Inference of User Types to Assist with More Inclusive Social Media Activism Campaigns

arXiv.org Artificial Intelligence

Social media provides a mechanism for people to engage with social causes across a range of issues. It also provides a strategic tool to those looking to advance a cause to exchange, promote or publicize their ideas. In such instances, AI can be either an asset if used appropriately or a barrier. One of the key issues for a workforce diversity campaign is to understand in real-time who is participating - specifically, whether the participants are individuals or organizations, and in case of individuals, whether they are male or female. In this paper, we present a study to demonstrate a case for AI for social good that develops a model to infer in real-time the different user types participating in a cause-driven hashtag campaign on Twitter, ILookLikeAnEngineer (ILLAE). A generic framework is devised to classify a Twitter user into three classes: organization, male and female in a real-time manner. The framework is tested against two datasets (ILLAE and a general dataset) and outperforms the baseline binary classifiers for categorizing organization/individual and male/female. The proposed model can be applied to future social cause-driven campaigns to get real-time insights on the macro-level social behavior of participants.


Event-Radar: Real-time Local Event Detection System for Geo-Tagged Tweet Streams

arXiv.org Artificial Intelligence

The local event detection is to use posting messages with geotags on social networks to reveal the related ongoing events and their locations. Recent studies have demonstrated that the geo-tagged tweet stream serves as an unprecedentedly valuable source for local event detection. Nevertheless, how to effectively extract local events from large geo-tagged tweet streams in real time remains challenging. A robust and efficient cloud-based real-time local event detection software system would benefit various aspects in the real-life society, from shopping recommendation for customer service providers to disaster alarming for emergency departments. We use the preliminary research GeoBurst as a starting point, which proposed a novel method to detect local events. GeoBurst+ leverages a novel cross-modal authority measure to identify several pivots in the query window. Such pivots reveal different geo-topical activities and naturally attract related tweets to form candidate events. It further summarises the continuous stream and compares the candidates against the historical summaries to pinpoint truly interesting local events. We mainly implement a website demonstration system Event-Radar with an improved algorithm to show the real-time local events online for public interests. Better still, as the query window shifts, our method can update the event list with little time cost, thus achieving continuous monitoring of the stream.


How Artificial Intelligence will Transform IT Operations and DevOps

#artificialintelligence

To state that DevOps and IT operations teams will face new challenges in the coming years sounds a bit redundant, as their core responsibility is to solve problems and overcome challenges. However, with the dramatic pace in which the current landscape of processes, technologies, and tools are changing, it has become quite problematic to cope with it. Moreover, the pressure business users have been putting on DevOps and IT operations teams is staggering, demanding that everything should be solved with a tap on an app. However, at the backend, handling issues is a different ball game; the users can't even imagine how difficult it is to find a problem and solve it. One of the biggest challenges IT operations and DevOps teams face nowadays is being able to pinpoint the small yet potentially harmful issues in large streams of Big Data being logged in their environment.


How Artificial Intelligence will Transform IT Operations and DevOps

#artificialintelligence

To state that DevOps and IT operations teams will face new challenges in the coming years sounds a bit redundant, as their core responsibility is to solve problems and overcome challenges. However, with the dramatic pace in which the current landscape of processes, technologies, and tools are changing, it has become quite problematic to cope with it. Moreover, the pressure business users have been putting on DevOps and IT operations teams is staggering, demanding that everything should be solved with a tap on an app. However, at the backend, handling issues is a different ball game; the users can't even imagine how difficult it is to find a problem and solve it. One of the biggest challenges IT operations and DevOps teams face nowadays is being able to pinpoint the small yet potentially harmful issues in large streams of Big Data being logged in their environment.


Twitter911: A Cautionary Tale

AAAI Conferences

Researchers have argued that social media, and in particular, Twitter, can be searched to improve “situational awareness” in emergency situations; that is, to provide objective, actionable real-time information to first-responders. Prior studies have examined cases of very rare, catastrophic emergencies that took place over many days, such as the aftermath of Hurricane Sandy. We asked instead if Twitter could pro- vide useful information for first-responders on a more regular basis, by conducting an exhaustive analysis of tweets and fire department data for medium-sized county (population 1 million), and for two larger-scale single-day emergencies in New York City. Our results are resoundingly negative: useful tweets were extraordinarily rare or nonexistence. This study provides a cautionary note as to the potential of Twitter and similar platforms for emergency situational awareness.


Schindler Holding's (SHLAF) CEO Thomas Oetterli on Q1 2017 Results - Earnings Call Transcript

#artificialintelligence

Welcome to the Schindler Conference Call on key figures for the First Quarter 2017. I'm here together with Erich Ammann, our CFO we will go into all financial details later during the call. As an introduction to the remark it is fair to say that we continued our successful plan of the last year as we kept our direction towards top-line growth and also higher profitability. Let's have a closer look on our highlights of the first quarter 2017 on slide two. We made further operational and strategic progress. First, we were able to confirm our growth path. Orders received increased by 5.9% in local currencies and also operating revenue rose by 3.8% in local currencies. Operating revenue was therefore within our guidance of 3% to 5% growth in 2017. Our investments into our geographic diversification mainly into our strategic markets were paying off. Second, we also continued to improve our profitability. The EBIT margin increased to 11.5% and even 11.7% before restructuring costs. Net profit stayed flat at CHF179 million due to some temporary booking losses on the ALSO exchangeable bond. Third, we made further progress in our strategic initiatives. We are on track with our globally harmonized modular product platforms, but it is still a long way to go to finalize this, and we were also able to successfully launch our new Internet of Elevator and Escalator Solutions, Schindler Ahead. Yesterday, we launched officially our new Schindler Ahead initiative and I would like to stay a little bit with that topic. As you can see on slide number 3, we will create significant customer benefits in the future. We increased the uptime of our equipment with predictive maintenance, we offer comprehensive insights about all type of information of the equipment for a better building, maintenance and management, and we generate convenience with superior customer service by interactive and personalized passenger experience. On slide four, you find the solution concept of our enhanced service offerings. There are four elements to be mentioned. First the Cube, The Cube enables machine intelligence, all relevant machine data are collected, filtered and transmitted to the cloud platform. The Cube is an intelligent device, not only a transmitter or a gateway, as we can run apps and stream multimedia content and handle emergency calls. The second topic is the cloud platform. The cloud platform creates real time insights.


A.I. NLP is changing banking for good. For everyone.

#artificialintelligence

You are on a two week long business trip across multiple locations. Its been 5 days and you are wondering how much you current trip has cost you yet. You tap on your banks app on your phone and type in - 'What's my spend in New York last week?' A bot answers back'a total of $343.54' The bot promptly marks it to your expense list to submit to your company and sends you an email report of the same.