Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define ground truth sepsis labels as features of severity scoring models is inherently circular and compromises the validity of the proposed approaches. We propose to create an independent ground truth for sepsis research by exploiting implicit knowledge of clinical practitioners via an electronic questionnaire which records attending physicians' daily judgements of patients' sepsis status. We show that despite its small size, our dataset allows to achieve state-of-the-art AUROC scores. An inspection of learned weights for standardized features of the linear model lets us infer potentially surprising feature contributions and allows to interpret seemingly counterintuitive findings.

The latest research Cognitive Computing Technology market is comprehensively and Insightful information in the report. The market report contains different market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. The report provides detailed profile assessments and multi-scenario revenue projections for the most promising industry participants. Each regional market studied in the report is carefully analyzed to explore key opportunities and business prospects they are expected to offer in the near future. This equips players with crucial information and data to improve their business tactics and ensure a strong foothold in the global Cognitive Computing Technology market.

Many consumers of geospatial and financial data spend hours each day processing, cleansing, and validating the data sets they use for analysis and model-building. This is true of many complex data sets that are highly relevant for global agricultural analysis, particularly those associated with weather, crop health (e.g. At Gro Intelligence, we manage those steps so our users are free to quickly develop models and insights without the delay of pre-processing and quality-checking the relevant data. Global Forecasting System (GFS), a weather model produced by the National Oceanic and Atmospheric Administration (NOAA), is a data source newly available in Gro. The complexity of GFS highlights how our platform can quickly download and process vast amounts of data.

The global artificial intelligence platforms market size is poised to reach USD 6.95 billion by 2023, according to a new report by Technavio, progressing at a CAGR of over 28% during the forecast period. This press release features multimedia. "Apart from the rise in demand for AI-based solutions, the rising adoption of AI-enabled chips, the increasing interoperability among neural networks, and increasing convergence of AI with IoT and blockchain, are some other major factors that will drive market growth during the forecast period," says a senior analyst at Technavio. The market is driven by the rise in demand for AI-based solutions. In addition, increasing investments in R&D for AI technology are anticipated to further boost the artificial intelligence platforms market during the forecast period.

The dreaded El Niño strikes the globe every 2 to 7 years. As warm waters in the tropical Pacific Ocean shift eastward and trade winds weaken, the weather pattern ripples through the atmosphere, causing drought in southern Africa, wildfires in South America, and flooding on North America's Pacific coast. Climate scientists have struggled to predict El Niño events more than 1 year in advance, but artificial intelligence (AI) can now extend forecasts to 18 months, according to a new study. The work could help people in threatened regions better prepare for droughts and floods, for example by choosing which crops to plant, says William Hsieh, a retired climate scientist in Victoria, Canada, who worked on early El Niño forecasts but who was not involved in the current study. Longer forecasts could have "large economic benefits," he says.

A former Google software engineer is sounding the alarm on killer robots. Laura Nolan resigned from Google last year when the tech giant started working with the U.S. military on drone technology, and since then, she has joined the Campaign to Stop Killer Robots, warning that autonomous robots with lethal capabilities could become a threat to humanity. Discussions concerning possibly banning autonomous weapons fell apart on August 21 during a United Nations meeting in Geneva, when Russian diplomats allegedly made a fuss over the language that was used in a document meant to begin the process of establishing a ban. "If you're a despot, how much easier is it to have a small cadre of engineers control a fleet of autonomous weapons for you than to have to keep your troops in line?" Nolan tells Inverse. "Autonomous weapons are potential weapons of mass destruction. They need to be made taboo in the same way that chemical and biological weapons are."

We introduce "extreme summarization," a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question "What is the article about?". We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset.

Whenever a social media user decides to share a story, she is typically pleased to receive likes, comments, shares, or, more generally, feedback from her followers. As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decides which stories to share next to receive more (positive) feedback. Under which conditions can she succeed? In this work, we first look into this problem from a theoretical perspective and then provide a set of practical algorithms to identify and characterize such behavior in social media. More specifically, we address the above problem from the viewpoint of sequential decision making and utility maximization. For a wide variety of utility functions, we first show that, to succeed, a user needs to actively trade off exploitation-- sharing stories which lead to more (positive) feedback--and exploration-- sharing stories to learn about her followers' preferences. However, exploration is not necessary if a user utilizes the feedback her followers provide to other users in addition to the feedback she receives. Then, we develop a utility estimation framework for observation data, which relies on statistical hypothesis testing to determine whether a user utilizes the feedback she receives from each of her followers to decide what to post next. Experiments on synthetic data illustrate our theoretical findings and show that our estimation framework is able to accurately recover users' underlying utility functions. Experiments on several real datasets gathered from Twitter and Reddit reveal that up to 82% (43%) of the Twitter (Reddit) users in our datasets do use the feedback they receive to decide what to post next.

The biggest impact DataRobot has had on Lenovo is that decisions are now made in a more proactive and precise way. As we get better and forecast accuracy keeps improving, people are becoming more confident and trusting of the process, the data, the models, and DataRobot.

With the rise of artificial intelligence and voice recognition technology, there has been a plenty discussion about how industries, the labour force, and business models will change, but how will these technologies change the way consumers and brands interact? The explosion of smartphones and social media opened a new world of opportunities to communicate. Mobile messaging apps like Facebook Messenger, WhatsApp, and WeChat became the norm. Chatbots followed, which allowed brands to communicate on a one-to-one, personal level. Voice recognition erupted in the same way as social media and is completely changing the way we interact.