Welcome to Small Humans, an ongoing series at Mashable that looks at how to take care of – and deal with – the kids in your life. Because Dr. Spock is nice and all, but it's 2018 and we have the entire internet to contend with. During my first pregnancy, I craved a mamaRoo rocker, believing it would alleviate by stress as a new mom. Instead, I did things the old-fashioned way, rocking my baby in my arms and using baby wraps for multitasking. Nearly three years later, I'm thinking about it again as we wait on our second child.

Accenture Introduces Ella and Ethan, AI Bots to Improve a Patient's Health and Care Using the Accenture Intelligent Patient Platform NEW YORK; Sept. 21, 2018 – Accenture (NYSE: ACN) has enhanced the Accenture Intelligent Patient Platform with the addition of Ella and Ethan, two interactive virtual-assistant bots that use artificial intelligence (AI) to constantly learn and make intelligent recommendations for interactions between life sciences companies, patients, health care providers (HCPs) and caregivers. Designed to help improve a patient's health and overall experience, the bots are part of Accenture's Salesforce Fullforce Solutions powered by Salesforce Health Cloud and Einstein AI, as well as Amazon's Alexa. The Ella and Ethan bots are part of the Patient Engagement Support solution in the Accenture Intelligent Patient Platform, a digital health solution that supports patients throughout their healthcare experience, from participation in clinical trials through managing ongoing treatment and wellness. The bots are designed to deliver a more personalized patient experience and better patient support. Ella is a virtual care assistant for patients that provides medication reminders, vitals tracking and appointment scheduling.

Accenture Introduces Ella and Ethan, AI Bots to Improve a Patient's Health and Care Using the Accenture Intelligent Patient Platform NEW YORK; Sept. 21, 2018 – Accenture (NYSE: ACN) has enhanced the Accenture Intelligent Patient Platform with the addition of Ella and Ethan, two interactive virtual-assistant bots that use artificial intelligence (AI) to constantly learn and make intelligent recommendations for interactions between life sciences companies, patients, health care providers (HCPs) and caregivers. Designed to help improve a patient's health and overall experience, the bots are part of Accenture's Salesforce Fullforce Solutions powered by Salesforce Health Cloud and Einstein AI, as well as Amazon's Alexa. The Ella and Ethan bots are part of the Patient Engagement Support solution in the Accenture Intelligent Patient Platform, a digital health solution that supports patients throughout their healthcare experience, from participation in clinical trials through managing ongoing treatment and wellness. The bots are designed to deliver a more personalized patient experience and better patient support. Ella is a virtual care assistant for patients that provides medication reminders, vitals tracking and appointment scheduling.

This is an era of disruption. Technology innovation, the intensifying march of digitization, and the cumulative effect of the "big exponentials" - the laws of accelerating growth governing processing power, storage, and bandwidth - are shattering, reshaping, and redefining economics in and across industries. Companies in the technology, media, and telecommunications (TMT) sectors are in the vanguard, bringing these new opportunities to market - even as their legacy businesses are threatened by them. TMT companies have begun to engage in end-to-end digital transformation by digitizing the value chain in their core businesses and enter new disruptive businesses. Still, they are subject to massive dislocation and attack.

In this paper, we propose a deep, globally normalized topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. Our model (1) captures discursive interactions along observed reply links in addition to traditional topic information, and (2) incorporates latent distributed representations arranged in a deep architecture, which enables a GPU-based mean-field inference procedure that scales efficiently to large data. We apply our model to a new social media dataset consisting of 13M comments mined from the popular internet forum Reddit, a domain that poses significant challenges to models that do not account for relationships connecting user comments. We evaluate against existing methods across multiple metrics including perplexity and metadata prediction, and qualitatively analyze the learned interaction patterns.

We present a study on predicting the factuality of reporting and bias of news media. While previous work has focused on studying the veracity of claims or documents, here we are interested in characterizing entire news media. These are under-studied but arguably important research problems, both in their own right and as a prior for fact-checking systems. We experiment with a large list of news websites and with a rich set of features derived from (i) a sample of articles from the target news medium, (ii) its Wikipedia page, (iii) its Twitter account, (iv) the structure of its URL, and (v) information about the Web traffic it attracts. The experimental results show sizable performance gains over the baselines, and confirm the importance of each feature type.

To cut a long story short, the movie was shot in beautiful Amsterdam, which was portrayed as a place of imminent catastrophe with empty streets and no people around. I thought that Deep Angel would have been a prime candidate for such a movie allowing the movie crew to shoot the scenes with people wandering around, cars passing by and then just run the film through Deep Angel to remove those unnecessary artefacts "auto-magically". No permission necessary to close down or evacuate streets, buildings and whole areas in order to shoot a film.

Qu Jianguo, 64, had a futuristic medical visit in Shanghai as he put his wrist through an automated pulse-taking machine and received the result within two minutes on a mobile phone – without a doctor present. The small device, which has a half-open clasp that records the heartbeat, is one of the technologies developed by hi-tech firms aiming to help China offset its shortage of physicians by combining big data and artificial intelligence (AI). The machine made by Ping An Good Doctor was shown off at the 2018 World AI Expo in Shanghai at a time when Chinese policymakers are making a major push to turn the country into a global tech leader. "I came here to see how Chinese-style medical treatment could be done without a doctor. That would be really convenient," said Qu, a retired IT worker attending the expo.

The Winograd Schema (WS) challenge has been proposed as an alternative to the Turing Test as a test for machine intelligence. In this short paper we "situate" the WS challenge in the data-information-knowledge continuum, suggesting in the process what a good WS is. Furthermore, we suggest that the WS is a special case of a more general phenomenon in language understanding, namely the phenomenon of the "missing text". In particular, we will argue that what we usually call thinking in the process of language understanding almost always involves discovering the missing text - text is rarely explicitly stated but is implicitly assumed as shared background knowledge. We therefore suggest extending the WS challenge to include tests beyond those involving reference resolution, including examples that require discovering the missing text in situations that are usually treated in computational linguistics under different labels, such as metonymy, quantifier scope ambiguity, lexical disambiguation, and co-predication, to name a few.

For too long, online consumers have been pitched the same kinds of clothes, the same types of opinions and the same sort of songs and over again, thanks to a like, an ad click or a Google search. We've been living in topical bubbles where our interest data is too often used to maintain our sensibilities rather than expand them. The fake news phenomenon is one of the biggest ramifications of these bubbles, but algorithms don't just impact our political leanings, they also influence our purchase decisions and almost everything we do with tech. What's more, an internal conflict among consumers puts businesses in a precarious position. On the one hand, 53 percent say they are concerned by data-driven ad retargeting and widespread support for new privacy legislation in GDPR and the California Consumer Privacy Act of 2018 makes it clear that people are wary of how marketers use their information.