Artificial intelligence and machine learning algorithms can help diagnose schizophrenia more quickly and accurately, according to research by the University of Alberta and IBM scientists. The research, published in May's npj Schizophrenia, with University of Alberta postdoctoral researcher Mina Gheiratmand as the primary author, was able to predict instances of schizophrenia with 74 per cent accuracy. The team, working out of the IBM Alberta Centre for Advanced Studies, also discovered the ability to predict the severity of specific symptoms in schizophrenia patients -- something that wasn't possible before. Schizophrenia doesn't currently have medical testing that can provide an absolute diagnosis, which can cause a significant delay before a symptomatic person is properly diagnosed. The chronic neurological disorder affects seven or eight of every 1,000 people and those with the disorder can experience hallucinations, movement disorders and cognitive impairments.

Amazon is having a rough week. The e-commerce powerhouse has celebrated a string of victories this year. Its stock price broke above $1,000 for the first time; it is presiding over an unprecedented retrenchment within the retail space as more than 8,000 brick-and-mortar stores are expected to close in the US this year, and the company announced plans to acquire yuppie favorite Whole Foods Market, promising to transform the company's stores into laboratories for automation and AI where advanced sensors will perform tasks previously reserved for human cashiers. It also revealed that its "Prime Day" sale was the "Biggest Global Shopping Event in Amazon History", surpassing Black Friday and Cyber Monday sales. But the string of good news came to an abrupt halt last week when Reuters reported that the top Democrat on the House antitrust subcommittee, David Civilline, has voiced concerns about Amazon's $13.7 billion plan to buy Whole Foods Market and requested in a letter to the House Judiciary Committee a hearing to examine the deal's potential impact on consumers – the first stirrings of what could metastasize into an anti-trust probe.

Consider two networks on overlapping, non-identical vertex sets. Given vertices of interest in the first network, we seek to identify the corresponding vertices, if any exist, in the second network. While in moderately sized networks graph matching methods can be applied directly to recover the missing correspondences, herein we present a principled methodology appropriate for situations in which the networks are too large for brute-force graph matching. Our methodology identifies vertices in a local neighborhood of the vertices of interest in the first network that have verifiable corresponding vertices in the second network. Leveraging these known correspondences, referred to as seeds, we match the induced subgraphs in each network generated by the neighborhoods of these verified seeds, and rank the vertices of the second network in terms of the most likely matches to the original vertices of interest. We demonstrate the applicability of our methodology through simulations and real data examples.

We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. We convert decoding - basically a discrete optimization problem - into a continuous optimization problem. The resulting constrained continuous optimisation problem is then tackled using gradient-based methods. Our powerful decoding framework enables decoding intractable models such as the intersection of left-to-right and right-to-left (bidirectional) as well as source-to-target and target-to-source (bilingual) NMT models. Our empirical results show that our decoding framework is effective, and leads to substantial improvements in translations generated from the intersected models where the typical greedy or beam search is not feasible. We also compare our framework against reranking, and analyse its advantages and disadvantages.

Google Analytics is a gold mine of data, and the backbone of many digital marketing processes. But with so many insights available, it can, at times, be difficult to find the points of most relevance. This has been a recurring theme in research reports relating to data usage – last year, a study from IBM found that just 3% of marketers categorized their ability to act on insights derived from customer data as'excellent', with 54% labeling it either'poor' or'very poor'. Yes, social media and digital channels have given us more access to data and information than ever, but with the rapid pace of change in the online space, many businesses are simply struggling to keep up, which is especially true of smaller organizations who have no capacity to hire data experts for such analysis. Google's well aware of this, and they're working to address it.

South Korea's LG Chem is slated to exclusively provide batteries for Apple's iPhone 9 which is likely to be launched next year, a Korean newspaper reported on Friday. LG Chem has invested hundreds of billions of won in a dedicated line for the purpose and will produce'L-shaped' batteries for next year's iPhone, the Korea Economic Daily reported citing an unnamed chemical industry source. A spokesman for LG Chem said it does not respond to inquiries about client companies. Apple could not be immediately reached for comment. LG Chem has invested hundreds of billions of won in a dedicated line for the Apple project, and will produce'L-shaped' batteries for next year's iPhone, the Korea Economic Daily claims.

Today, Intel launched the Movidius Neural Compute Stick, the world's first USB-based deep learning inference kit and self-contained artificial intelligence (AI) accelerator that delivers dedicated deep neural network processing capabilities to a wide range of host devices at the edge. Designed for product developers, researchers and makers, the Movidius Neural Compute Stick aims to reduce barriers to developing, tuning and deploying AI applications by delivering dedicated high-performance deep-neural network processing in a small form factor. As more developers adopt advanced machine learning approaches to build innovative applications and solutions, Intel is committed to providing the most comprehensive set of development tools and resources to ensure developers are retooling for an AI-centric digital economy. Whether it is training artificial neural networks on the Intel Nervana cloud, optimizing for emerging workloads such as artificial intelligence, virtual and augmented reality, and automated driving with Intel Xeon Scalable processors, or taking AI to the edge with Movidius vision processing unit (VPU) technology, Intel offers a comprehensive AI portfolio of tools, training and deployment options for the next generation of AI-powered products and services. "The Myriad 2 VPU housed inside the Movidius Neural Compute Stick provides powerful, yet efficient performance – more than 100 gigaflops of performance within a 1W power envelope – to run real-time deep neural networks directly from the device," said Remi El-Ouazzane, vice president and general manager of Movidius, an Intel company.

We are living in the Age of Plastic. In 2015, the world's industries created 448 million tons of it -- twice as much as it did in 1998. However, our recycling efforts have not matched pace. In fact, according to the EPA, barely 14 percent of plastic products are recycled globally. But a new generation of recycling technology is here to keep the world's plastics in circulation and out of our landfills.

What do we all have in common? Surprisingly, the answer is menopause. But scientists still haven't quite figured out why this phenomenon exists. After all, if the purpose of evolution is to make sure that we most effectively pass on our genes, then why would women stop reproducing after a certain age? A study published on Thursday in PLOS Computational Biology may offer some insight into the evolution of menopause in humans.

Robots could soon be caring for dementia patients, a report suggested. Technological advancements mean gadgets and apps are already being introduced into dementia care. Devices like GPS trackers are used to ensure patients do not get lost, while monitors in the home can detect falls and tell if the phone or television is being used, sending information back to family and carers. Some dementia patients wear smartwatches, which collect data on their sleep and eating patterns, blood pressure and sugar levels for analysis by doctors, researcher and carers. And interactive technology, including virtual reality and specialised apps and computer games have been introduced to care homes to help calm patients with dementia. Electronic calendars and speaking reminders can also help patients tell the time or remind them to do simple tasks like washing hands.