"The game isn't going to tell you if you got that right." It's an ominous way to start a game demo, but one I'm not too surprised by after playing 80 Days and Sorcery! Heaven's Vault is Inkle's new game, and I met up with studio co-founders Jon Ingold and Joseph Humfrey recently to get some hands-on time. At first glance it's a huge step away from Inkle's previous games--where 80 Days and Sorcery mostly played out in text, Heaven's Vault features fully navigable 3D environments. Your character Aliya Elasra is 2D though, her movements more suggested by a series of still frames than fully animated.

SYS-CON Events announced today that Loom Systems will exhibit at SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Founded in 2015, Loom Systems delivers an advanced AI solution to predict and prevent problems in the digital business. Loom stands alone in the industry as an AI analysis platform requiring no prior math knowledge from operators, leveraging the existing staff to succeed in the digital era. With offices in San Francisco and Tel Aviv, Loom Systems works with customers across industries around the world. The widespread success of cloud computing is driving the DevOps revolution in enterprise IT.

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.

Newswire) Artificial Intelligence (AI) is one of the next big growth areas for the mobile industry according to a white paper released by IHS Markit (Nasdaq: INFO), a world leader in critical information, analytics and solutions, ahead of Mobile World Congress. "Smartphones will both be the interface for consumer AI and deliver the vast amount of data technology companies need to train AI systems" "We see AI making smart devices even smarter with improved user experiences," said Ian Fogg, director of mobile and telecom analysis at IHS Markit. "Existing AI agents like Apple's Siri and Google Assistant will expand across the industry, complemented by embedded AI in all parts of mobile devices from cameras, to audio, to machine." "Smartphones will both be the interface for consumer AI and deliver the vast amount of data technology companies need to train AI systems," Fogg said. Software investments and partnerships are critical for hardware companies to create smarter AI-enabled experiences, said the IHS Markit white paper.

Synechron, the global financial services consulting and technology company, announced Thursday (March 23) the launch of "Neo," a set of artificial intelligence (AI)-based tools for the financial services industry. In a press release, Synechron said that with Neo, financial institutions will be able deploy AI solutions that solve complex business challenges. "Financial institutions are looking to implement the latest technology to address real-world problems in financial services. Neo and Synechron's AI Accelerators will be pivotal in helping clients be at the forefront of technological advancement, while providing a comprehensive set of tools to ease and streamline processes. This will allow businesses to deploy technology-enabled processes that augment the role of individuals, allowing them to be elevated to higher-value business tasks," said Faisal Husain, Synechron co-founder & CEO, in the press release.

Military delegates arrive at the Great Hall of the People for a meeting ahead of Saturday's opening ceremony of the National People's Congress (NPC), in Beijing, China March 4, 2016. As Washington fiddles, China is investing billions in U.S. startups with cutting-edge products that could have military applications at the same time it is dialing back investments in less critical American industries such as entertainment. A New York Times story this week says that among the startups are companies working on artificial intelligence for military robots, rocket engines, ship sensors and printers that could produce high-tech components such as computer screens for military jets. Many of the firms making such investments are owned by companies controlled by the Chinese government or connected to its leaders. A blog post last December on the website of CB Insights, which tracks startup investments, says that China poured $9.9 billion into new Silicon Valley firms in 2015 and made an additional $3.5 billion in tech investments in the first nine months of last year.

While millions of people fearing a robot run world, it is Americans who should worry the most. A new report has found that 38 percent of US jobs will be replaced by robots and artificial intelligence by the early 2030s. The analysis, by accountancy giant PwC, has also revealed that it is financials service jobs that are at most risk of a robot takeover - 61 percent could be replaced by machines. While millions of people fearing a robot run world – it is Americans that should worry the most. PwC found that 4 in 10 jobs in the US are at risk of being replaced by robots.

This paper introduces a method for efficiently inferring a high-dimensional distributed quantity from a few observations. The quantity of interest (QoI) is approximated in a basis (dictionary) learned from a training set. The coefficients associated with the approximation of the QoI in the basis are determined by minimizing the misfit with the observations. To obtain a probabilistic estimate of the quantity of interest, a Bayesian approach is employed. The QoI is treated as a random field endowed with a hierarchical prior distribution so that closed-form expressions can be obtained for the posterior distribution. The main contribution of the present work lies in the derivation of \emph{a representation basis consistent with the observation chain} used to infer the associated coefficients. The resulting dictionary is then tailored to be both observable by the sensors and accurate in approximating the posterior mean. An algorithm for deriving such an observable dictionary is presented. The method is illustrated with the estimation of the velocity field of an open cavity flow from a handful of wall-mounted point sensors. Comparison with standard estimation approaches relying on Principal Component Analysis and K-SVD dictionaries is provided and illustrates the superior performance of the present approach.

Integrating visual and linguistic information into a single multimodal representation is an unsolved problem with wide-reaching applications to both natural language processing and computer vision. In this paper, we present a simple method to build multimodal representations by learning a language-to-vision mapping and using its output to build multimodal embeddings. In this sense, our method provides a cognitively plausible way of building representations, consistent with the inherently re-constructive and associative nature of human memory. Using seven benchmark concept similarity tests we show that the mapped vectors not only implicitly encode multimodal information, but also outperform strong unimodal baselines and state-of-the-art multimodal methods, thus exhibiting more "human-like" judgments---particularly in zero-shot settings.

Objective: We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classification task for determining whether a patient has a medical condition, and is a crucial part of secondary analysis of healthcare data. We assess the performance of deep learning algorithms and compare them with classical NLP approaches. Materials and Methods: We compare convolutional neural networks (CNNs), n-gram models, and approaches based on cTAKES that extract pre-defined medical concepts from clinical notes and use them to predict patient phenotypes. The performance is tested on 10 different phenotyping tasks using 1,610 discharge summaries extracted from the MIMIC-III database. Results: CNNs outperform other phenotyping algorithms in all 10 tasks. The average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our model having an F1-score up to 37 points higher than alternative approaches. We additionally assess the interpretability of our model by presenting a method that extracts the most salient phrases for a particular prediction. Conclusion: We show that NLP methods based on deep learning improve the performance of patient phenotyping. Our CNN-based algorithm automatically learns the phrases associated with each patient phenotype. As such, it reduces the annotation complexity for clinical domain experts, who are normally required to develop task-specific annotation rules and identify relevant phrases. Our method performs well in terms of both performance and interpretability, which indicates that deep learning is an effective approach to patient phenotyping based on clinicians' notes.