The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. Here we systematically reviewed publications, where deep learning approaches and neuroimaging data were used for diagnostic classification of AD. A PubMed and google scholar search was performed to find deep learning papers for AD published between January 2013 and July 2018, which were reviewed, evaluated, and classified by algorithms and neuroimaging types, and findings were summarized. The diagnostic classification of AD using deep learning approaches and neuroimaging data was examined in 16 studies. The approach to combine traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection has produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD. Deep learning approaches such as convolutional neural network (CNN) or recurrent neural network (RNN) using neuroimaging data without preprocessing for feature selection have yielded accuracies of up to 96.0% for AD classification and 84.2% for MCI conversion prediction. Furthermore, the best classification performance was obtained when multimodal neuroimaging data as well as fluid biomarkers were integrated. Deep learning approaches without preprocessing neuroimaging data for feature selection, a major bottleneck of traditional machining learning in high-dimensional data, continue to improve their performance and to show great promise in the diagnostic classification of AD using multimodal neuroimaging data.

Using AI and lots of data Stefan Heck's startup Nauto is already saving lives on the road. While completely autonomous vehicles all the time may still be months away, this venture has found a way to apply the best in technology to make a difference today. Stefan Heck grew up between New York City and Austria. He learned to navigate ski slopes before he was three years old. Now he's helping large commercial fleets, taxi companies, automakers, and insurers navigate the applications of new technology on the roads.

Dr. Stefanie Chiras is the vice president and general manager of the Red Hat Enterprise Linux Business Unit at Red Hat. She joined Red Hat in July 2018 and has worldwide business responsibility for the successful definition, execution, and delivery of the Red Hat Enterprise Linux product line. Prior to this role she was the vice president of offering management for IBM Cognitive Systems. As part of the Cognitive Systems brand team, she led the worldwide business for the systems portfolio, AIX, IBM i, Linux, the system software portfolio, and the cloud stack. Within that responsibility she drove the strategic implementation of transforming the portfolio to support both enterprise workloads as well as AI, ML and DL and ensuring the portfolio delivered leadership in both.

The National Institute of Standards and Technology is seeking industry input on ways to develop standards for artificial intelligence. Under a federal executive order issued on February 11, NIST is to develop the plan within 180 days. "Timely and fit-for-purpose AI technical standards, whether developed by national or international organizations, will play a crucial role in the development and deployment of AI technologies, and will be essential to building trust and confidence about AI technologies and for achieving economies of scale," according to the agency. Consequently, NIST will host a workshop on May 30 in Gaithersburg, Md., as well as a webcast. Issues that NIST seeks to better understand include current status and plans regarding the availability, use and development of AI technical standards and tools in support of reliable, robust and trustworthy systems that use AI; needs and challenges regarding the existence, availability, use and development of AI standards and tools; and the role of federal agencies in finding tools to meet the nation's needs.

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A link has been posted to your Facebook feed. The Strong National Museum of Play announced the 2019 inductees to the World Video Game Hall of Fame on Thursday and added four new games to its roster. Established in 2015, the World Video Game Hall of Fame honors all kinds of electric games – computer, handheld, console, arcade and mobile – and has recognized 24 games since its founding. Criteria for inductees examines four categories: the game's icon status (is it widely recognized?), longevity (is it more than a passing fad?), geographical reach (are people playing it all over the world?) and influence (has the game affected the industry as a whole, popular culture or society in general?). And the 2019 inductees are... (drumroll, please!) "Microsoft Solitaire" debuted in 1990 on the Windows 3.0 computing platform and became ubiquitous around the world, according to a news release from The Strong Museum.

Background: Autism spectrum disorder (ASD) is currently diagnosed using qualitative methods that measure between 20-100 behaviors, can span multiple appointments with trained clinicians, and take several hours to complete. In our previous work, we demonstrated the efficacy of machine learning classifiers to accelerate the process by collecting home videos of US-based children, identifying a reduced subset of behavioral features that are scored by untrained raters using a machine learning classifier to determine children's "risk scores" for autism. We achieved an accuracy of 92% (95% CI 88%-97%) on US videos using a classifier built on five features. Objective: Using videos of Bangladeshi children collected from Dhaka Shishu Children's Hospital, we aim to scale our pipeline to another culture and other developmental delays, including speech and language conditions. Methods: Although our previously published and validated pipeline and set of classifiers perform reasonably well on Bangladeshi videos (75% accuracy, 95% CI 71%-78%), this work improves on that accuracy through the development and application of a powerful new technique for adaptive aggregation of crowdsourced labels. We enhance both the utility and performance of our model by building two classification layers: The first layer distinguishes between typical and atypical behavior, and the second layer distinguishes between ASD and non-ASD. In each of the layers, we use a unique rater weighting scheme to aggregate classification scores from different raters based on their expertise. We also determine Shapley values for the most important features in the classifier to understand how the classifiers' process aligns with clinical intuition. Results: Using these techniques, we achieved an accuracy (area under the curve [AUC]) of 76% (SD 3%) and sensitivity of 76% (SD 4%) for identifying atypical children from among developmentally delayed children, and an accuracy (AUC) of 85% (SD 5%) and sensitivity of 76% (SD 6%) for identifying children with ASD from those predicted to have other developmental delays. Conclusions: These results show promise for using a mobile video-based and machine learning–directed approach for early and remote detection of autism in Bangladeshi children.

Harnessing machine learning to improve health is a major ambition for both medical practitioners and the healthcare industry. If the two can join forces on a global scale in 2019, with the right investment and the right approach, AI could propel a revolution to democratise global health and to leapfrog access to health services in low- and middle-income countries. A chronic shortage of human resources is one of the major obstacles to better health and healthcare in many resource-poor settings. When it comes to global health, artificial intelligence offers huge opportunities to fill the gap left by critical healthcare worker shortages, particularly if combined with mobile phone technology. For example, social enterprises such as Peek Vision can use smart-phone based technology to enable healthcare providers to deliver cost-effective and targeted treatment to people with eyesight problems.

My infant son is a supercomputer. I'm not saying he could beat a grandmaster at chess, a trick that tech companies roll out to show us how smart their machines are, but what he can do better than even the most advanced artificial intelligence (AI) system on the planet is to learn. Which is why he -- in common with all babies and children up to four years old -- has suddenly become of great interest to Silicon Valley. The top AI researchers, including those at Google, are turning to our little ones in their quest to make the next big breakthrough.