England's Grand National run at Aintree is gruelling. It has 30 fences, two with open ditches, in a distance of 2.25 miles that's completed twice. AI has just moved up the field in the eHealth equivalent. An AI project at London's Moorfields Eye Hospital with Google's DeepMind has accurately diagnosed eye conditions from scans. As ophthalmologists' workloads and their complexities increase, diagnostic imaging is expanding faster than specialists can interpret the results.

Give Jason Holmberg 10,000 zebra photos and he'll find the specific individual zebra you're looking for, no problem. "It could take two minutes," he said. Holmberg is executive director of the nonprofit Wild Me. The Portland-based organization has developed a digital tool called Wildbook that uses artificial intelligence and machine learning to expedite wildlife identification. In tandem with citizen science, Wildbook is able to condense years of human work -- like photographing thousands of animals and identifying each by hand -- into a matter of weeks.

Running high-resolution physical models is computationally expensive and essential for many disciplines. Agriculture, transportation, and energy are sectors that depend on high-resolution weather models, which typically consume many hours of large High Performance Computing (HPC) systems to deliver timely results. Many users cannot afford to run the desired resolution and are forced to use low resolution output. One simple solution is to interpolate results for visualization. It is also possible to combine an ensemble of low resolution models to obtain a better prediction. However, these approaches fail to capture the redundant information and patterns in the low-resolution input that could help improve the quality of prediction. In this paper, we propose and evaluate a strategy based on a deep neural network to learn a high-resolution representation from low-resolution predictions using weather forecast as a practical use case. We take a supervised learning approach, since obtaining labeled data can be done automatically. Our results show significant improvement when compared with standard practices and the strategy is still lightweight enough to run on modest computer systems.

And a pinnacle of success in the context of technological advancements is the field of artificial intelligence, also called AI. In current times, AI is utilized immensely in a variety of sectors such as health, e-commerce, retail, automotive, defense and security, chemical plants, packaging, construction, and BFSI. This indicates that AI has found immense potential in myriad applications, thus giving rise to the global artificial intelligence and cognitive computing market. Rising disposable incomes and changing lifestyles have primarily caused an increase in the demand for enhanced systems to exist in various walks of life. Such a high demand is propelling the market to experience extensive growth too. A phenomenal progress in the development of computers as well as internet facilities has primarily been responsible for improving different functions of systems, wherein a computer is highly needed.

Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue . They are based on the statistical analysis of accumulated and moving window data subsets with construction of a kurtosis-skewness diagram. This approach was applied to the data gathered by the wearable heart monitor for various types and levels of physical activities, and for people with various physical conditions. The different levels of physical activities, loads, and fitness can be distinguished from the kurtosis-skewness diagram, and their evolution can be monitored. Several metrics for estimation of the instant effect and accumulated effect (physical fatigue) of physical loads were proposed. The data and results presented allow to extend application of these methods for modeling and characterization of complex human activity patterns, for example, to estimate the actual and accumulated physical load and fatigue, model the potential dangerous development, and give cautions and advice in real time.

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and predicting informal settlements using only street intersections data, regardless of the variation of urban form, number of floors, materials used for construction or street width. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and a machine learning approach, using Multinomial Logistic Regression (MNL) and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics that these regions tend to be filled in with smaller subdivided lots of housing relative to the formal areas within the local context, and the paucity of services and infrastructure within the boundary of these settlements that require relatively bigger lots. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context.

Designing a reliable natural language (NL) interface for querying tables has been a longtime goal of researchers in both the data management and natural language processing (NLP) communities. Such an interface receives as input an NL question, translates it into a formal query, executes the query and returns the results. Errors in the translation process are not uncommon, and users typically struggle to understand whether their query has been mapped correctly. We address this problem by explaining the obtained formal queries to non-expert users. Two methods for query explanations are presented: the first translates queries into NL, while the second method provides a graphic representation of the query cell-based provenance (in its execution on a given table). Our solution augments a state-of-the-art NL interface over web tables, enhancing it in both its training and deployment phase. Experiments, including a user study conducted on Amazon Mechanical Turk, show our solution to improve both the correctness and reliability of an NL interface.

Society is rife with fears about the future of AI. For some, like Richard Branson and Ray Dalio, it's AI's exacerbation of the wealth gap and the looming social crisis it could bring about. My worry for AI, however, is that we are vilifying a technology that may in fact be our single greatest resource for creating a just world--if we build it right. While there do exist serious ethical conundrums and uncertainties regarding the potential for artificial superintelligence, at the moment, what we are facing on Earth is mostly a complex assemblage of human problems. It is true that automation is contributing to the increasing inequality within most countries, with the income gap widening between each country's educated middle and upper classes and their less-educated lower classes.

A look at how Yemen's brutal civil war is creating a market for prosthetic limbs. Each is missing a vital part of their body – a hand, a leg, an arm. Inside that building is new hope for each: Prosthetic limbs are being cut, carved, melted and molded. Young patient recently outfitted with a new leg waits for his training session outside the Ma'rib prosthetics center in Yemen (Fox News/Hollie McKay) "Sometimes I go to my office to cry for each of these miserable stories," Dr. Haitham Ahmed Ali Ahmed, a Sudanese volunteer with Physicians Across Continents, told Fox News. "It isn't fair, but we do whatever we can to give them another chance."

Deep in the rainforest in a northern corner of the Republic of Congo, some of the most sophisticated monitoring of animal sounds on earth is taking place. Acoustic sensors are collecting large amounts of data around the clock for the Elephant Listening Project. These sensors capture the soundscape in Nouabalé-Ndoki National Park and adjacent logging areas: chimpanzees, gorillas, forest buffalo, endangered African grey parrots, fruit hitting the ground, blood-sucking insects, chainsaws, engines, human voices, gunshots. But researchers and local land managers who placed them there are listening for one sound in particular -- the calls of elusive forest elephants. Forest elephants are in steep decline; scientists estimate two-thirds of Africa's population has likely been lost to ivory poaching in recent decades.