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The dreaded El Niño strikes the globe every 2 to 7 years. As warm waters in the tropical Pacific Ocean shift eastward and trade winds weaken, the weather pattern ripples through the atmosphere, causing drought in southern Africa, wildfires in South America, and flooding on North America's Pacific coast. Climate scientists have struggled to predict El Niño events more than 1 year in advance, but artificial intelligence (AI) can now extend forecasts to 18 months, according to a new study. The work could help people in threatened regions better prepare for droughts and floods, for example by choosing which crops to plant, says William Hsieh, a retired climate scientist in Victoria, Canada, who worked on early El Niño forecasts but who was not involved in the current study. Longer forecasts could have "large economic benefits," he says.

In the last ten years, 2 billion people were affected by disasters according to the World Disasters report 2018. In 2017, 201 million people needed humanitarian assistance and 18 million were displaced due to weather related disasters. Many of these disaster-prone areas are literally "missing" from the map, making it harder for first responders to prepare and deliver relief efforts. Since the inception of Tasking Manager, the Humanitarian OpenStreetMap Team (HOT) community has mapped at an incredible rate with 11 million square kilometers mapped in Africa alone. However, large parts of Africa with populations prone to disasters still remain unmapped -- 60% of the 30 million square kilometers.

Held annually in Asia, Europe and the Middle East, Hack In The Box conferences bring together the world's top cyber security experts to share and discuss their latest knowledge, ideas and techniques with security professionals and students. The next HITB event is HITB CyberWeek, which takes place October 12th – 17th at Emirates Palace, Abu Dhabi. As usual, it will offer security trainings, talks, and live challenges. Among the live competitions to be held at the conference is the AI Challenge, which aimed at machine learning and AI enthusiasts. In the first one, participants are tasked with developing an automated penetration testing model based on the DeepExploit framework.

Reach Robotics, the company behind the spider-like MekaMon robot you might've seen on the shelves at the Apple Store, is closing down. Billed as the "world's first gaming robot," MekaMon is part video game, part STEM tool. You could plop it down on the carpet and point your phone at it to battle virtual augmented reality enemies, face off against other MekaMon owners in multiplayer battles or build custom programs for the robot on top of Apple's Swift Playgrounds. Here's a video we did on Reach Robotics a few years back: Reach Robotics was founded in 2013. They released their MekaMon robot in November of 2017, just a few months after raising a $7.5 million Series A. The consumer robotics sector is an inherently challenging space – especially for a start-up.

While technology was involved in less than 50% of customer experience (CX) projects in the past, now it is involved in almost two-thirds of these types of initiatives. This is according to Don Scheibenreif, distinguished VP analyst within the customer experience research group at Gartner, commenting on the evolution of customer experience over the last 10 years. With technology, many organisations are realising it's not enough to have a good customer service department or marketing department, they are thinking about the whole experience a client goes through, stated Scheibenreif. He pointed to the fact that an airline can now offer customers 10 different ways to interact with it via technology, something that was not around five years ago. "Technology is providing companies more choices to interact with the customer. Software vendors are also responding to those needs by creating things like data warehouses for customer data. There are more intelligent case management systems that figure out the next customer actions, as well as interactive and predictive e-commerce platforms. "A lot of the technology has evolved to provide companies many more options to engage with customers and even potentially predict what they will do next." The distinguished VP analyst added Gartner believes companies like Starbucks, Domino's Pizza, Ritz-Carlton Hotel and BMW have found value in using technology. "These are organisations that actively study customer behaviour, they understand customer journeys and they are very strategic in their use of technology to create an overall experience," he said. This week, Gartner hosted its annual Gartner Symposium/ITxpo in Cape Town, where CIOs and senior IT executives heard about the technology and trends shaping the future of IT and business, including how firms cannot grow their business if they don't give customers great experiences. For Gartner, Scheibenreif said, CX is the sum of either the one-off or cumulative interactions that a person would have with a company, whether it's through employees, physical location or the company Web site. When you put all of those interactions together, that is the customer experience. Mapping out the customer journey, the Gartner analyst said it started from customer service. "It was basically improving interactions on the phone, first-call resolution and being able to provide a personalised experience.

All the same, it's one thing employers love to hate. Whenever an employer hires a worker, the understanding is that they have offered a fair wage for what you have to offer. Therefore, any time spent outside work should be for recuperating (recreation and sleep) from the busy day. That has been the thinking: "Eight hours labour, eight hours recreation, eight hours rest." It might have worked in the early 20th century, during the industrial revolution, but a rethink for the demands of the 21st century is long due.

All the same, it's one thing employers love to hate. Whenever an employer hires a worker, the understanding is that they have offered a fair wage for what you have to offer. Therefore, any time spent outside work should be for recuperating (recreation and sleep) from the busy day. That has been the thinking: "Eight hours labour, eight hours recreation, eight hours rest." It might have worked in the early 20th century, during the industrial revolution, but a rethink for the demands of the 21st century is long due.

We introduce "extreme summarization," a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question "What is the article about?". We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset.

Reducing traffic accidents is an important public safety challenge, therefore, accident analysis and prediction has been a topic of much research over the past few decades. Using small-scale datasets with limited coverage, being dependent on extensive set of data, and being not applicable for real-time purposes are the important shortcomings of the existing studies. To address these challenges, we propose a new solution for real-time traffic accident prediction using easy-to-obtain, but sparse data. Our solution relies on a deep-neural-network model (which we have named DAP, for Deep Accident Prediction); which utilizes a variety of data attributes such as traffic events, weather data, points-of-interest, and time. DAP incorporates multiple components including a recurrent (for time-sensitive data), a fully connected (for time-insensitive data), and a trainable embedding component (to capture spatial heterogeneity). To fill the data gap, we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. By employing the US-Accidents dataset and through an extensive set of experiments across several large cities, we have evaluated our proposal against several baselines. Our analysis and results show significant improvements to predict rare accident events. Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.