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As urban populations grow, more people are exposed to the benefits and hazards of city life. One challenge for cities is managing the risk of disasters in a constantly changing built environment. Buildings, roads, and critical infrastructure need to be mapped frequently, accurately, and in enough detail to represent assets important to every community. Knowing where and how assets are vulnerable to damage or disruption by natural hazards is key to disaster risk management (DRM). The Global Facility for Disaster Reduction and Recovery (GFDRR) is a global partnership that provides knowledge, funding, and technical assistance towards achieving the vision of a world where resilient societies manage and adapt to ever-changing disaster and climate risk, and where the human and economic impact of disasters is reduced.

Video surveillance systems are evolving and are using artificial intelligence (AI) to inspect and analyse video footage, interpret patterns and flag unusual activity. Lenovo DCG and Pivot3 provide a state-of-the-art upgraded infrastructure solutions that aim to enhance current technology required to support these systems rather than entrusting the preservation of crucial data to outdated NVR technology. Commenting on the partnership, Dr. Chris Cooper, General Manager for Lenovo DCG, Middle East, Turkey and Africa, said, "We are delighted to showcase our partnership with Pivot3 at one the world's leading technology trade shows. The Middle East is exhibiting tremendous growth in terms of adopting smart solutions. The UAE in particular is investing heavily in implementing the latest innovations in their technological infrastructure; therefore, we see great potential from our partnership with Pivot3 as we work together to supply the appetite for next generation computing products and services."

Regardless of whether it's language, music, speech, or video, sequential data isn't simple for AI and machine learning models to understand, especially when it relies upon the extensive surrounding context. For example, if an individual or an item vanishes from view in a video just to return a lot later, numerous algorithms will overlook what it looked like. Researchers at Google set out to solve this with Transformer, a design that reached out to thousands of words, drastically improving performance in tasks like song composition, image synthesis, sentence-by-sentence text translation, and document summarization. In any case, Transformer isn't flawless by any stretch, extending it to bigger contexts makes clear its restrictions. Applications that utilize enormous windows have memory necessities going from gigabytes to terabytes in size, which means models can just ingest a few paragraphs of text or create short bits of music.

Researchers in Japan and The Netherlands have, for the first time, used machine learning techniques, in particular artificial neural networks (ANNs), to predict the Indian Ocean Dipole (IOD), a positive phase of which has affected weather and climate in India and Australia in a spectacular fashion so far in 2019-20. The IOD has both positive and negative phases, and signals large socio-economic impacts on many countries and hence predicting the IOD well in advance will benefit the affected societies, note authors JV Ratnam and Swadhin K Behera (Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama) and HA Dijkstra (Institute for Marine and Atmospheric Research Utrecht, Utrecht University in The Netherlands) in a paper published by Nature. The IOD is a mode of climate variability observed in the Indian Ocean sea surface temperature anomalies with one pole in Sumatra (Indonesia) and the other near East Africa. Therefore, the IOD is represented by an index derived from the gradient between the western equatorial Indian Ocean and the south-eastern equatorial Indian Ocean. It starts sometime in May-June, peaks in September-October and ends in November (2019's rather strong positive phase of the IOD lasted into early January of 2020).

Interferometric Synthetic Aperture Radar (InSAR) imagery based on microwaves reflected off ground targets is becoming increasingly important in remote sensing for ground movement estimation. However, the reflections are contaminated by noise, which distorts the signal's wrapped phase. Demarcation of image regions based on degree of contamination ("coherence") is an important component of the InSAR processing pipeline. We introduce Convolutional Neural Networks (CNNs) to this problem domain and show their effectiveness in improving coherence-based demarcation and reducing misclassifications in completely incoherent regions through intelligent preprocessing of training data. Quantitative and qualitative comparisons prove superiority of proposed method over three established methods.

We present a method for learning options from segmented demonstration trajectories. The trajectories are first segmented into skills using nonparametric Bayesian clustering and a reward function for each segment is then learned using inverse reinforcement learning. From this, a set of inferred trajectories for the demonstration are generated. Option initiation sets and termination conditions are learned from these trajectories using the one-class support vector machine clustering algorithm. We demonstrate our method in the four rooms domain, where an agent is able to autonomously discover usable options from human demonstration. Our results show that these inferred options can then be used to improve learning and planning.

As of December 2019, there are more than 400 unicorns worldwide - that's according to the aptly named Global Unicorn Club report from CB Insights. A significant number of those, private companies valued at more than $1bn for the uninitiated, are firmly entrenched in the financial services sector. One such company is Lemonade. The New York-based business uses artificial intelligence, chatbots and other innovative technologies to disrupt the home and rental insurance sectors. It recently appeared in FinTech magazine's Top 10 Unicorns list, which provided a comprehensive snapshot of those most innovative companies to achieve this coveted status.

SOMEBODY LESS driven than Tom Siebel would have long since thrown in the towel. In 2006 the entrepreneur, then 53 years old, sold his first firm, Siebel Systems, which made computer programs to track customer relations, to Oracle, a giant of business software. That left him a billionaire--but a restless one. In 2009, a few months after Mr Siebel had launched a new startup, he was trampled by an elephant while on safari in Tanzania. When, a dozen surgeries later, he could work again, the enterprise almost went bankrupt.

NAIROBI (Thomson Reuters Foundation) - Countries are rapidly developing "killer robots" - machines with artificial intelligence (AI) that independently kill - but are moving at a snail's pace on agreeing global rules over their use in future wars, warn technology and human rights experts. From drones and missiles to tanks and submarines, semi-autonomous weapons systems have been used for decades to eliminate targets in modern day warfare - but they all have human supervision. Nations such as the United States, Russia and Israel are now investing in developing lethal autonomous weapons systems (LAWS) which can identify, target, and kill a person all on their own - but to date there are no international laws governing their use. "Some kind of human control is necessary ... Only humans can make context-specific judgements of distinction, proportionality and precautions in combat," said Peter Maurer, President of the International Committee of the Red Cross (ICRC).