Huawei has launched a South African-based Cloud and Artificial Intelligence (AI) Innovation Centre to drive innovation, knowledge transfer and economic growth through app development in the AI industry. The announcement was made by Ray Rui, president of Huawei Cloud Africa region, during the Huawei Cloud Summit Africa 2020, an online event to unpack the opportunities of cloud computing for African business under the theme "Building an Intelligent Africa". "AI will be critical to social evolution and industrial growth in future," said Rui. "We also believe that when you grow economic opportunities, everyone benefits. For this reason, we are opening the Huawei Cloud & AI Innovation Centre to application developers across all economic sectors." The new centre will be based at Huawei's South African headquarters in Woodmead, Johannesburg, but developers across Africa will be able to access the centre remotely. It will teach AI application best practice, link developers to markets, support AI supply chains, develop talent and support application innovation.

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally and has become a pandemic. People have lost their lives due to the virus and the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop machine learning techniques to predict the spread of COVID-19. Prediction of the spread can allow counter measures and actions to be implemented to mitigate the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models were trained and tested on novel coronavirus 2019 dataset, which contains 19.53 Million confirmed cases of COVID-19. Our proposed models were evaluated by using Mean Absolute Error and compared with baseline method. Our experimental results, both quantitative and qualitative, demonstrate the superior prediction performance of the proposed models.

With histograms as its foundation, we develop Categorical Exploratory Data Analysis (CEDA) under the extreme-$K$ sample problem, and illustrate its universal applicability through four 1D categorical datasets. Given a sizable $K$, CEDA's ultimate goal amounts to discover by data's information content via carrying out two data-driven computational tasks: 1) establish a tree geometry upon $K$ populations as a platform for discovering a wide spectrum of patterns among populations; 2) evaluate each geometric pattern's reliability. In CEDA developments, each population gives rise to a row vector of categories proportions. Upon the data matrix's row-axis, we discuss the pros and cons of Euclidean distance against its weighted version for building a binary clustering tree geometry. The criterion of choice rests on degrees of uniformness in column-blocks framed by this binary clustering tree. Each tree-leaf (population) is then encoded with a binary code sequence, so is tree-based pattern. For evaluating reliability, we adopt row-wise multinomial randomness to generate an ensemble of matrix mimicries, so an ensemble of mimicked binary trees. Reliability of any observed pattern is its recurrence rate within the tree ensemble. A high reliability value means a deterministic pattern. Our four applications of CEDA illuminate four significant aspects of extreme-$K$ sample problems.

The most reliable predictor of a relationship's success is partners' belief that the other person is fully committed, a Western University-led international research team has found. Other important factors in a successful relationship include feeling close to, appreciated by and sexually satisfied with your partner, says the study – the first-ever systematic attempt at using machine-learning algorithms to predict people's relationship satisfaction. "Satisfaction with romantic relationships has important implications for health, wellbeing and work productivity," Western Psychology professor Samantha Joel said. "But research on predictors of relationship quality is often limited in scope and scale, and carried out separately in individual laboratories." The massive machine-learning study, conducted by Joel, Paul Eastwick from University of California, Davis, and 84 other scholars from around the world, delved into more than 11,000 couples and 43 distinct self-reported datasets on romantic couples.

During the pandemic, robots are the literal breadwinners. As Samir Menon, the founder and CEO of robotics startup Dexterity, told Fortune, the "coronavirus took us a bit on a wild ride." Dexterity, a roughly two-and-a-half-year-old startup that's raised $56.2 million in funding from investors like Kleiner Perkins and Lightspeed Venture Partners, specializes in software that makes industrial robots, like mechanical grippers and machines that pick-and-place items, more capable. Among the customers that have relied on Dexterity's technology during the pandemic is Bimbo Bakeries, which produces some well-known baked goods via brands like Sara Lee, Oroweat, and Boboli. Because Dexterity is working with Bimbo Bakeries, the startup has been deemed an "essential business," since the food supply chain needs to continue operating unperturbed amid the lockdowns.

Communication has become increasingly dynamic with the popularization of social networks and applications that allow people to express themselves and communicate instantly. In this scenario, distributed representation models have their quality impacted by new words that appear frequently or that are derived from spelling errors. These words that are unknown by the models, known as out-of-vocabulary (OOV) words, need to be properly handled to not degrade the quality of the natural language processing (NLP) applications, which depend on the appropriate vector representation of the texts. To better understand this problem and finding the best techniques to handle OOV words, in this study, we present a comprehensive performance evaluation of deep learning models for representing OOV words. We performed an intrinsic evaluation using a benchmark dataset and an extrinsic evaluation using different NLP tasks: text categorization, named entity recognition, and part-of-speech tagging. Although the results indicated that the best technique for handling OOV words is different for each task, Comick, a deep learning method that infers the embedding based on the context and the morphological structure of the OOV word, obtained promising results.

The most reliable predictor of a relationship's success is partners' belief that the other person is fully committed, a Western University-led international research team has found. Other important factors in a successful relationship include feeling close to, appreciated by and sexually satisfied with your partner, says the study – the first-ever systematic attempt at using machine-learning algorithms to predict people's relationship satisfaction. "Satisfaction with romantic relationships has important implications for health, wellbeing and work productivity," Western Psychology professor Samantha Joel said. "But research on predictors of relationship quality is often limited in scope and scale, and carried out separately in individual laboratories." The massive machine-learning study, conducted by Joel, Paul Eastwick from University of California, Davis, and 84 other scholars from around the world, delved into more than 11,000 couples and 43 distinct self-reported datasets on romantic couples.

If you saw a finch one time, chances are you'd have great difficulty picking it out from a large group of finches later on. A new artificial intelligence-based system can do just that, though, potentially making life much easier for both biologists and the birds that they study. Ordinarily, if a wildlife biologist wants to track an individual bird, they have to capture it, put an identity band on its leg, release it, then later recapture it to read that band. Needless to say, doing so is quite a hassle for the scientist, and very stressful to the bird. There are now also remotely readable GPS tags, although these still have to initially be attached to the animal.

Trying to identify a wild bird while frantically leafing through a bird-spotters' guide is no easy task. But modern technology has come to the rescue, with artificial intelligence trained to help out amateur twitchers. Where people may be confused by two similar looking birds, or a juvenile which does not yet have its adult plumage, AI has been found to identify birds with up to around 90 per cent accuracy. The technology was trained using pictures of wild great tits and sociable weavers, as well as captive zebra finches. It works in a similar way to the face-recognition programmes used to identify people in crowds.

For even the most sharp-eyed of ornithologists, one great tit can look much like another. But now researchers have built the first artificial intelligence tool capable of identifying individual small birds. Computers have been trained to learn to recognise dozens of individual birds – which could potentially save scientists arduous hours in the field with binoculars, as well as the catching of birds to fit coloured rings to their legs. "We show that computers can consistently recognise dozens of individual birds, even though we cannot ourselves tell these individuals apart," said André Ferreira, a PhD student at the Centre for Functional and Evolutionary Ecology (CEFE-CNRS), in France. "In doing so, our study provides the means of overcoming one of the greatest limitations in the study of wild birds – reliably recognising individuals."