The University of Bristol (UoB) has partnered with Bristol Zoological Society (BZS) to develop a trailblazing approach to wildlife conservation, harnessing the power of machine learning and drone technology to transform wildlife conservation around the world. Backed by the Cabot Institute for the Environment, BZS and EPSRC's CASCADE grant, a team of researchers travelled to Cameroon in December last year to test a number of drones, sensor technologies and deployment techniques to monitor the critically endangered Kordofan giraffe populations in Bénoué National Park. "There has been significant and drastic decline recently of larger mammals in the park and it is vital that accurate measurements of populations can be established to guide our conservation actions," said Dr Gráinne McCabe, head of field conservation and science at BZS. "Bénoué National Park is very difficult to patrol on foot and large parts are virtually inaccessible, presenting a huge challenge for wildlife monitoring. What's more, the giraffe are very well camouflaged and often found in small, transient groups," said Dr Caspian Johnson, conservation science lecturer at BZS. Striving to uncover the best method for airborne wildlife monitoring, BZS reached out to Dr Matt Watson from the UoB's School of Earth Sciences, and Dr Tom Richardson from the University's Aerospace Department, as well as a member of the Bristol Robotics Laboratory (BRL). The team forged successful collaborations using drones to monitor and measure volcanic emissions to create a system for wildlife monitoring.

NAIROBI, Jan 31 (Thomson Reuters Foundation) - Ugandan doctors are giving new mothers artificial intelligence-enabled devices to remotely monitor their health in a first-of-its-kind study aiming to curb thousands of preventable maternal deaths across Africa, medics and developers said. Doctors at Mbarara Hospital in western Uganda will give devices to more than 1,000 women who have undergone caesarean section births to wear on their upper arms at all times. Algorithms detect at-risk cases and alert doctors. Joseph Ngonzi from Mbarara University of Science and Technology, which is conducting the study, said it would help "improve monitoring in a resource-constrained environment". The World Health Organization says almost 300,000 women worldwide die annually from preventable causes related to pregnancy and childbirth - that's more than 800 women every day.

Drug discovery is the preliminary step in the process of a novel drug identification and its therapeutic target. Artificial intelligence (AI) is commonly used in the healthcare industry for drug discovery. Artificial intelligence technology has the ability to recognize drug targets, and play a significant role in drug design, discovery, identification and screening of molecules instantly and effectively. Drug discovery or new drug target are being estimated based on potency, bioavailability, efficacy, and toxicity. The AI for drug discovery market is expected to grow during the forecast period due to the increasing number of cross-industry partnerships & collaborations, a significant growth in venture capital investments, rise in importance of drug discovery and increase in funding of the R&D activities for the use of AI technology in the field of drug discovery. However, limited awareness, unwillingness among medical practitioners to adopt AI-based technologies, unclear regulatory guidelines for medical software and lack of interoperability among AI solutions offered by different vendors are likely to hamper the growth of the market in the forecast period.

The below excerpt showcases the distinctiveness and acumen of a holistic AI company – TransOrg Analytics that is consistently striving to roll out intelligent and scalable solutions for the betterment of its customers. TransOrg Analytics is an award-winning player in'Analytics and Advisory' space. Founded in 2009, TransOrg is headquartered in Gurugram, India with a global presence in the US, UK, Singapore, India and the Middle East. Its global clientele includes Fortune 500 companies and industry leaders in sectors like Banking, Financial Services, Insurance, Telecom, Hospitality, CPG, Retail, E-commerce, Travel & Aviation. TransOrg has a strong team of over 80 high-performing Data Scientists, Data Engineers, Visualization experts from top schools and leadership with strong academic credentials and collective work experience of over 100 years with reputed organizations.

A team of researchers from the University of La Rochelle in France have converted albatrosses into de facto surveillance drones as part of a project to gather data on illegal fishing boats in the South Pacific and Indian Ocean. The team traveled to popular albatross nesting locations at Amsterdam Island and Kerguelen Island in the Indian Ocean north of Antarctica, and attached small sensors to 169 albatrosses in a procedure that took about 10 minutes per bird. The sensors weigh 65 grams, or around a seventh of a pound, and were equipped with a GPS receiver, a radar antenna, and a satellite communications monitor to track various boat communication systems. The devices were each powered by a small lithium battery that maintains a charge through a small solar panel, according to a report from ArsTechnica. The albatrosses covered more than 18 million square miles between East Africa and New Zealand, gathering data from more than 600,000 GPS locations.

Attention is defined as the preparedness for the mental selection of certain aspects in a physical environment. In the computer vision domain, this mechanism is of most interest, as it helps to define the segments of an image/video that are critical for obtaining a specific decision. This paper introduces one 'implicit' attentional mechanism for deep learning frameworks, that provides simultaneously: 1) masks-free; and 2) foreground-focused samples for the inference phase. The main idea is to generate synthetic data composed of interleaved segments from the original learning set, while using class information only from specific segments. During the learning phase, the newly generated samples feed the network, keeping their label exclusively consistent with the identity from where the region-of-interest was cropped. Hence, as the model receives images of each identity with inconsistent unwanted areas, it naturally pays the most attention to the label consistent consistent regions, which we observed to be equivalent to learn an effective receptive field. During the test phase, samples are provided without any mask, and the network naturally disregards the detrimental information, which is the insight for the observed improvements in performance. As a proof-of-concept, we consider the challenging problem of pedestrian re-identification and compare the effectiveness of our solution to the state-of-the-art techniques in the well known Richly Annotated Pedestrian (RAP) dataset. The code is available at https://github.com/Ehsan-Yaghoubi/reid-strong-baseline.

--One problem found when working with satellite images is the radiometric variations across the image and different images. Intending to improve remote sensing models for the classification of burnt areas, we set two objectives. The first is to understand the relationship between feature spaces and the predictive ability of the models, allowing us to explain the differences between learning and generalization when training and testing in different datasets. We find that training on datasets built from more than one image provides models that generalize better . These results are explained by visualizing the dispersion of values on the feature space. The second objective is to evolve hyper-features that improve the performance of different classifiers on a variety of test sets. We find the hyper-features to be beneficial, and obtain the best models with XGBoost, even if the hyper-features are optimized for a different method. Deforestation has serious implications on biodiversity, on rural communities that depend on forests for survival, and on greenhouse gas emissions that drive the global climate. The machine learning (ML) community can help by providing predictive models that, after learning from a small sample of an image, can automatically classify the whole image. Although previous ML work in forest monitoring has shown good results, the predictive models are often applied on the same location where they were learnt, i.e., the models are trained and tested in samples from the same dataset (e.g., [1]) or time series from the same area (e.g., [2]).

Personality has been identified as a vital factor in understanding the quality of human robot interactions. Despite this the research in this area remains fragmented and lacks a coherent framework. This makes it difficult to understand what we know and identify what we do not. As a result our knowledge of personality in human robot interactions has not kept pace with the deployment of robots in organizations or in our broader society. To address this shortcoming, this paper reviews 83 articles and 84 separate studies to assess the current state of human robot personality research. This review: (1) highlights major thematic research areas, (2) identifies gaps in the literature, (3) derives and presents major conclusions from the literature and (4) offers guidance for future research.

Seaborne robots have made a startling discovery beneath a 20-mile glacier in Alaska. The technology found the massive rivers of ice may be melting under the LeConte Glacier much faster than previously thought. Scientists programmed autonomous kayaks to swim near the icy cliffs of the glacier to measure the'ambient meltwater intrusions', which shows how much fresh water is flowing into the ocean from underneath the glacier. The study found ambient melting was 100 times higher than models had estimated. This is the first time experts have been able to analyze plumes of meltwater - the water released when snow or ice melts, where glaciers meet the ocean- because the feat is far too dangerous for ships due to falling ice of slabs from the glacier.

If ever there was a white paradise, it was Silicon Valley in the 1980s. We called them geniuses and wizards. Industry titans, and even a few free-thinking hippies who believed they were gods, powerful enough to shape technology to their will. This white cast of characters populated the world's largest high tech hub at a rate of nearly 75%. Absorbed 80% of the area's generated income.