The following post is from Neha Goel, Champion of student competitions and online data science competitions. She's here to promote a new Deep Learning challenge available to everyone. If you win, you get money, plus a bonus if you use MATLAB. We at MathWorks, in collaboration with DrivenData, are excited to bring you this challenge. Through this challenge you'll be working with a real-world dataset of drone aerial imagery (big images) for classification.

The following post is from Neha Goel, Champion of student competitions and online data science competitions. She's here to promote a new Deep Learning challenge available to everyone. If you win, you get money, plus a bonus if you use MATLAB. We at MathWorks, in collaboration with DrivenData, are excited to bring you this challenge. Through this challenge you'll be working with a real-world dataset of drone aerial imagery (big images) for classification.

Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks. Current such "meta-optimizers" often learn in the space of continuous optimization algorithms that are point-based and uncertainty-unaware. To overcome the limitations, we propose a meta-optimizer that learns in the algorithmic space of both point-based and population-based optimization algorithms. The meta-optimizer targets at a meta-loss function consisting of both cumulative regret and entropy. Specifically, we learn and interpret the update formula through a population of LSTMs embedded with sample- and feature-level attentions. Meanwhile, we estimate the posterior directly over the global optimum and use an uncertainty measure to help guide the learning process. Empirical results over non-convex test functions and the protein-docking application demonstrate that this new meta-optimizer outperforms existing competitors.

There is an emerging trend in the reinforcement learning for healthcare literature. In order to prepare longitudinal, irregularly sampled, cli nical datasets for reinforcement learning algorithms, many researchers will resa mple the time series data to short, regular intervals and use last-observation- carried-forward (LOCF) imputation to fill in these gaps. Typically, they will not mai ntain any explicit information about which values were imputed. In this work, w e (1) call attention to this practice and discuss its potential implication s; (2) propose an alternative representation of the patient state that addresses som e of these issues; and (3) demonstrate in a novel but representative clinical data set that our alternative representation yields consistently better results for ach ieving optimal control, as measured by off-policy policy evaluation, compared to repr esentations that do not incorporate missingness information.

This week, amazing video showed protesters on the streets of Chile teaming up to bring down a police drone with what appear to be simple pocket lasers. The footage shows a huge group of people aiming around 40 or 50 green handheld lasers at a police drone hovering overhead. After about 20 seconds of being targeted by the communal green laser beam, the drone appears to malfunction and slowly falls toward the ground. Yet, as it descends out of the laser's line of sight, the drone appears to momentarily regain control, Around ten seconds later, protester re-aim their group laser at the drone and it finally drops all the way into the crowd. Just how pocket lasers were able to cause a drone to malfunction remains unclear.

Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., English, have a vast arrayof these resources, most under-resourced languages do not, especially for fine-grained sentiment tasks, such as aspect-level or targeted sentiment analysis. To improve this situation, we propose a cross-lingual approach to sentiment analysis that is applicable to under-resourced languages and takes into account target-level information. This model incorporates sentiment information into bilingual distributional representations, byjointly optimizing them for semantics and sentiment, showing state-of-the-art performance at sentence-level when combined with machine translation. The adaptation to targeted sentiment analysis on multiple domains shows that our model outperforms other projection-based bilingual embedding methods on binary targetedsentiment tasks. Our analysis on ten languages demonstrates that the amount of unlabeled monolingual data has surprisingly little effect on the sentiment results. As expected, the choice of a annotated source language for projection to a target leads to better results for source-target language pairs which are similar. Therefore, our results suggest that more efforts should be spent on the creation of resources for less similar languages tothose which are resource-rich already. Finally, a domain mismatch leads to a decreased performance. This suggests resources in any language should ideally cover varieties of domains.

SAN FRANCISCO, Nov. 14, 2019 (GLOBE NEWSWIRE) -- Zendrive, a mission-driven company using data and analytics to make roads safer and insurance fairer, today announced John Kramer as Director of Insurance Sales. He brings with him nearly 20 years of insurance experience in underwriting, usage-based insurance, product management, and connected car technology. "Zendrive is an established leader in driving analytics and research, with the world's largest driving data set of over 180 billion miles," said John Kramer. "The company is thinking critically about how to apply its unique, predictive telematics factors and innovative technology solutions to the insurance industry. I'm proud to join such a passionate team powering a modern, data-driven future alongside our insurance provider partners."

Around the globe, a majority of Millennial parents say they are very likely to seek out a doctor using AI for cancer diagnoses should their child or a family member need such an evaluation. A majority of Millennial parents in China (94%), India (88%) and Brazil (78%) would be very likely to seek out a doctor using AI for cancer diagnoses for their child or a family member, while 59% of U.K. parents and 53% of U.S. parents are very likely to do so.

The IEEE Generation AI 2019: Third Annual Study of Millennial Parents and Generation Alpha Kids illuminates the trust Millennial parents in the U.S., U.K., India, China and Brazil with Generation Alpha children (nine years-old or younger) have in using AI and emerging technologies for the health and wellness of their children. Born from 2010-2025, Generation Alpha is considered to be the most tech-infused demographic to date. Explore below how the future of health and wellness technologies will transform our modern medical practices and impact the lives of families using them.

Newswire) Investorideas.com, a global investor news source covering Artificial Intelligence issues a special edition of The AI Eye, reporting on recent news from VSBLTY Groupe Technologies Corp. (CSE:VSBY) (OTC: VSBGF) (5VS.F), a leading retail software and technology company using artificial intelligence. VSBLTY Groupe Technologies Corp. (CSE:VSBY) (5VS.F) (OTC:VSBGF) and intelligent lighting solutions provider Energetika's smart city contract with Mexico City has begun deployment. Investorideas.com caught up with VSBLTY co-founder and CEO, Jay Hutton for an interview in which he explained how this smart city contract goes further than what is standard. Hutton said that while smart city solutions typically cover commercial properties, VSBLTY and Energetika are also bringing application to residential spaces. "When you look at smart cities, often you see a focus on commercial applications," he said.