A total of 3,400 people lost their lives and 14,670 were injured in fires in the USA in 2017, a 9.6 percent increase over the 2007 casualty rate. Floods meanwhile killed more than 100 Americans last year, a number that has also been increasing. To counter this terrible toll, artificial intelligence researchers are developing systems to improve disaster prediction accuracy and provide timely evacuation guidance for panicked people during emergencies. Providing real-time evacuation strategies is critical, as research shows that in emergencies many people tend to wait for instructions when they should already be proceeding to an exit. Simulation systems can play a valuable role in identifying and testing evacuation plans to enable individuals to promptly leave a dangerous area via the safest and fastest route.

"Time series models are used to forecast future events based on previous events that have been observed (and data collected) at regular time intervals." We will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside. Time series forecasting is a technique for the prediction of events through a sequence of time. The technique is used across many fields of study, from geology to behavior to economics. The techniques predict future events by analyzing the trends of the past, on the assumption that future trends will hold similar to historical trends.

As artificial intelligence algorithms and systems become more sophisticated and take on bigger responsibilities, it becomes more and more important to ensure that AI systems avoid dangerous, unwanted behavior. Recently a team of researchers from the University of Massachusetts Amherst and Stanford published a paper that demonstrates how specific AI behavior can be avoided, through the use of a technique that elicits precise mathematical instructions that can be used to tweak the behavior of an AI. According to TechXplore, the research was predicated on the assumption that unfair/unsafe behaviors can be defined with mathematical functions and variables. If this is true then it should be possible for researchers to train systems to avoid these specific behaviors. The research team aimed to develop a toolkit that could be employed by users of the AI to specify which behaviors they want the AI to avoid, and enable AI engineers to reliably train a system that will avoid unwanted actions when used in real-world scenarios.

We've all tried Google Street View before, but what if you could explore the world and see faraway places through the eyes of a roving machine? At the recent Combined Exhibition of Advanced Technologies (CEATEC) outside Tokyo, telepresence robots equipped with displays showing their remote users were turning heads on the show floor. These simple machines are basically webcams on wheels, but they formed a striking example of how a system that combines hardware in the physical world with online users and cloud-based artificial intelligence will become part of everyday life. Akira Fukabori, director of ANA HOLDINGS INC.'s Avatar Division, shows off an all-terrain Avatar robot at CEATEC 2019. Developed by OhmniLabs and ANA HOLDINGS INC., the parent company of All Nippon Airways, the newme Avatar telepresence robots are up to 150 cm tall and roll around on a wheeled base at speeds up to 2.9 kph.

Close to a thousand machine learning papers are published each and every week. On Fridays, Synced selects seven studies from the last seven days that present topical, innovative or otherwise interesting or important research that we believe may be of special interest to our readers. Author: Liang Yang, Xi-Zhu Wu, Yuan Jiang, Zhi-Hua Zhou from National Key Laboratory for Novel Software Technology, Nanjing University Abstract: In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models. Deep neural network methods usually jointly embed the feature and label information into a latent space to exploit label correlations. However, the success of these methods highly depends on the precise choice of model depth.

Bio: Veysel Kocaman is a Senior Data Scientist and ML Engineer having a decade long industry experience. He is currently working on his PhD in CS at Leiden University (NL) and holds an MS degree in Operations Research from Penn State University.

Education and Healthcare are the first two fields to benefit from deep learning where the impact will be significant and benefit overall humanity. It took a deep learning algorithm a fraction of a second to identify chimps and classify their genders. Expert human labelers were given nearly an hour to complete the same task.

From Alibaba and Baidu to Google, Facebook, and Microsoft, China and the United States produced virtually every one of the top consumer AI companies in the world today. That leaves Europe trailing behind the U.S. and China, even though Europe still has the largest community of cited AI researchers. Startup founders, analysts, and organizations seeking to bring ecosystems together for collective action pondered how the European AI ecosystem can catch up with China and the United States at TechBBQ, a gathering of hundreds of Nordic tech startups held recently in Copenhagen. Presenters argued that Europe has to turn things around not just for the good of the European economy, but also to provide the world with an alternative to the corporate-driven approach of the U.S. and the state-driven approach of China. "If you look today at some of the spending, which is devoted to artificial intelligence and frontier technologies, we're pretty much squeezed between the U.S. and now China, and China is leading," said Jacques Bughin, a senior advisor at the McKinsey Global Institute.

How ready is India for the world of artificial intelligence (AI)? This question is answered in one of the latest global lists researched and created by Oxford Insights and commissioned by Canada's International Development Research Centre. It is called the Government Artificial Intelligence Readiness Index. The just released index measures 194 countries on a scale of 1-10 on how ready their governments are to embrace and make use of a world dominated by artificial intelligence. At the very top of the list is Singapore with a score of 9.186 and at the bottom is Somalia which scores 0.168. This kind of ranking is critical to understand the adoption of a technology which has, famously, been described as the'next electricity' or as fundamental as electricity by Andrew Ng, co-founder of Coursera and former head of Google Brain.

Artificial Intelligence (AI) is a key driver for innovation in the global digital reality market, which includes immersive technologies such as virtual reality (VR), augmented reality (AR), X Reality (XR or Cross Reality) and Artificial Intelligence (AI) itself. Various reports project that advancements in technology such as 5G, artificial intelligence, edge computing, and robotics are expected to transform the augmented and virtual reality experiences in the near future. One by ResearchAndMarkets stated that: "The immersive technology market, including augmented and virtual reality, is expected to see huge growth in the next 5 years. Technological advancements such as 5G and artificial intelligence will transform the augmented and virtual experiences in the future. Some of the significant impacts of 5G across sectors could be enabling virtually crafted workplaces and fully interactive and emulating in-office work environments. Active companies in the markets this week include Micron Technology, Inc. (NASDAQ: MU), Hawkeye Systems, Inc. (OTCQB: HWKE), NVIDIA Corporation (NASDAQ: NVDA), Intel Corporation (NASDAQ: INTC), International Business Machines Corporation (NYSE: IBM). The report projected that: "The AR and VR market revenue is expected to reach $55.01 billion by 2021.