Media


Former CNN Exec Klein Brings News on Artificial Intelligence - Broadcasting & Cable

#artificialintelligence

Why This Matters: While TV companies tout navigation, Silicon Valley giants are using AI, personalization to draw viewers to OTT. Leave it to a reporter to find a good navigation tool. Former CBS News and CNN executive Jon Klein believes the TV business needs artificial intelligence to compete with the digital giants whose streaming and over-the-top video offering are accumulating viewers and revenues. Klein is worth listening to. At CNN, he was an early adopter of social media as a newsgathering tool.


Algorithm-Driven Design: How Artificial Intelligence Is Changing Design

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Yury leads a team comprising UX and visual designers at one of the largest Russian Internet companies, Mail.Ru Group. Upgrade your inbox and get our editors' picks twice a month. Digital products are getting more and more complex. In this article, Yury Vetrov explains why we need to support more platforms, tweak usage scenarios for more user segments, and hypothesize more. I've been following the idea of algorithm-driven design for several years now and have collected some practical examples. The tools of the approach can help us to construct a UI, prepare assets and content, and personalize the user experience. The information, though, has always been scarce and hasn't been systematic. However, in 2016, the technological foundations of these tools became easily accessible, and the design community got interested in algorithms, neural networks and artificial intelligence (AI). Now is the time to rethink the modern role of the designer.


Accenture Introduces Ella and Ethan, AI Bots to Improve a Patient's Health and Care Using the Accenture Intelligent Patient Platform

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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

#artificialintelligence

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.


How AI Can Inspire Consumers and Build Stronger Brand Loyalty

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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.


Neo4j 3.5 Poised to Power the Next Generation of AI & Machine Learning Systems

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Neo4j, the market leader in connected data, announced today the upcoming release of Neo4j 3.5, the native graph platform designed to drive the success and adoption of real-time business applications, including artificial intelligence (AI) and machine learning (ML) systems. Neo4j customers – including eBay and Caterpillar – have demonstrated that connected graph datasets are a foundational element of enterprise AI applications. Graph-based data models provide the necessary context for AI applications by capturing facts related to and relationships among people, processes, applications, data and machines. Informed by successful AI customer deployments – including knowledge graphs, fraud detection, recommendation systems and conversation engines – Neo4j 3.5 delivers foundational features for AI-powered systems of connection to generate bottom-line business value. "The way we organize and represent knowledge in AI-powered systems has a profound effect on what and how they can learn," said Bowles.


Incorporating Behavioral Constraints in Online AI Systems

arXiv.org Artificial Intelligence

AI systems that learn through reward feedback about the actions they take are increasingly deployed in domains that have significant impact on our daily life. However, in many cases the online rewards should not be the only guiding criteria, as there are additional constraints and/or priorities imposed by regulations, values, preferences, or ethical principles. We detail a novel online agent that learns a set of behavioral constraints by observation and uses these learned constraints as a guide when making decisions in an online setting while still being reactive to reward feedback. To define this agent, we propose to adopt a novel extension to the classical contextual multi-armed bandit setting and we provide a new algorithm called Behavior Constrained Thompson Sampling (BCTS) that allows for online learning while obeying exogenous constraints. Our agent learns a constrained policy that implements the observed behavioral constraints demonstrated by a teacher agent, and then uses this constrained policy to guide the reward-based online exploration and exploitation. We characterize the upper bound on the expected regret of the contextual bandit algorithm that underlies our agent and provide a case study with real world data in two application domains. Our experiments show that the designed agent is able to act within the set of behavior constraints without significantly degrading its overall reward performance.


How Deep Learning is Personalizing the Internet - Dataconomy

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Deep learning is a subfield of machine learning and it comprises several approaches to tackling the single most important goal of AI research: allowing computers to model our world well enough to exhibit something like what we humans call intelligence. On a basic conceptual level, deep learning approaches share a very basic trait. DL algorithms interpret the raw data through multiple processing layers. Each of these layers takes the output of the previous one as its input and creates a more abstract representation of it. As a result, the more data is being fed into the right algorithm, the more general are the rules and features that it's able to infer in relation to a given scenario and, therefore, the apter it gets at handling new, similar situations.


A Simple but Hard-to-Beat Baseline for Session-based Recommendations

arXiv.org Machine Learning

Convolutional Neural Networks (CNNs) models have been recently introduced in the domain of top-$N$ session-based recommendations. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional latent matrix, and treated as an image. The convolution and pooling operations are then applied to the mapped item embeddings. In this paper, we first examine the typical session-based CNN recommender and show that both the generative model and network architecture are suboptimal when modeling long-range dependencies in the item sequence. To address the issues, we propose a simple, but very effective generative model that is capable of learning high-level representation from both short- and long-range dependencies. The network architecture of the proposed model is formed of a stack of holed convolutional layers, which can efficiently increase the receptive fields without relying on the pooling operation. Another contribution is the effective use of residual block structure in recommender systems, which can ease the optimization for much deeper networks. The proposed generative model attains state-of-the-art accuracy with less training time in the session-based recommendation task. It accordingly can be used as a powerful session-based recommendation baseline to beat in future, especially when there are long sequences of user feedback.


Superhighway: Bypass Data Sparsity in Cross-Domain CF

arXiv.org Machine Learning

Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains. Many traditional methods focus on enriching compared neighborhood relations in CF directly to address the sparsity problem. In this paper, we propose superhighway construction, an alternative explicit relation-enrichment procedure, to improve recommendations by enhancing cross-domain connectivity. Specifically, assuming partially overlapped items (users), superhighway bypasses multi-hop inter-domain paths between cross-domain users (items, respectively) with direct paths to enrich the cross-domain connectivity. The experiments conducted on a real-world cross-region music dataset and a cross-platform movie dataset show that the proposed superhighway construction significantly improves recommendation performance in both target and source domains.