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."

The major Artificial Intelligence Chipsets producing areas include North America, Europe, Asia-Pacific, Middle-East, and Africa. Artificial Intelligence Chipsets industry states and outlook (2020-2027) is introduced in this part. Additionally, Artificial Intelligence Chipsets market dynamics stating the opportunities, market risk, and key driving forces are researched. Part 2: This part covers Artificial Intelligence Chipsets manufacturers profile based On their small business overview, product type, and program. Additionally, the sales volume, Artificial Intelligence Chipsets product cost, gross margin analysis, and Artificial Intelligence Chipsets market share of each participant is profiled in this report.

Autonomous driving is one of the most sought-after market in tech right now. Along other major changes in the automotive industry such as electric vehicles, connected cars, or ridesharing, autonomous driving is at the heart of what is considered to bethe second inflection point of mobility with a promise of a greener, safer, more convenient, and cheaper transportation. Indeed, just like we turned from horses to cars about a 100 years ago, mobility is slowly turning from mechanical transportation machines to supercomputers on wheels; creating a new land of opportunities for outsiders to come in and for balances of power to shift drastically in a trillion dollar automotive industry. "Autonomous driving is at the heart of what is considered the second inflection point of mobility." Since autonomous driving activities kicked off with the DARPA challenge in 2004, the ecosystem became a lot larger and fiercely competitive with OEMs and tier 1 suppliers now joined by internet companies, TELCOs, electronics manufacturers, and a large crowd of startups.

Tensor data with rich structural information becomes increasingly important in process modeling, monitoring, and diagnosis. Here structural information is referred to structural properties such as sparsity, smoothness, low-rank, and piecewise constancy. To reveal useful information from tensor data, we propose to decompose the tensor into the summation of multiple components based on different structural information of them. In this paper, we provide a new definition of structural information in tensor data. Based on it, we propose an additive tensor decomposition (ATD) framework to extract useful information from tensor data. This framework specifies a high dimensional optimization problem to obtain the components with distinct structural information. An alternating direction method of multipliers (ADMM) algorithm is proposed to solve it, which is highly parallelable and thus suitable for the proposed optimization problem. Two simulation examples and a real case study in medical image analysis illustrate the versatility and effectiveness of the ATD framework.