Science is in the midst of a data crisis. Last year, there were more than 1.2 million new papers published in the biomedical sciences alone, bringing the total number of peer-reviewed biomedical papers to over 26 million. However, the average scientist reads only about 250 papers a year. Meanwhile, the quality of the scientific literature has been in decline. Some recent studies found that the majority of biomedical papers were irreproducible.

Exponential progress in artificial intelligence (AI) and machine learning, fuelled by the combination of cloud, big data and new algorithms, is transforming the retail industry. As AI leverages big data to automate, predict and personalize, retail is testing and implementing these applications to garner robust competitive advantages. The key focus for AI in retail is customer relationships. In times of concerns for the retail sector in the UK where sales posted biggest quarterly fall since 2010, the refashioning of this industry comes as a breath of fresh air with many opportunities to come. Global Artificial Intelligence Opportunities in Retail, 2017, new research from Frost & Sullivan's Connected Industries Growth Partnership Service, offers an overview of AI and its relevance to business in 2017.

Description We are seeking a Project Manager to join our company and intelligently impact the lives of millions of patients. We are seeking someone passionate about client success and client engagement. ABOUT AICURE At AiCure, we build and deploy advanced artificial intelligence technologies to optimize patient behavior and medication adherence. The company was founded in 2010 to revolutionize patient monitoring with the ultimate goal of keeping the patients we love alive longer and out of the hospital. Our advanced AI platform is now used in clinical research and high-risk patient populations to ensure that medication is being taken properly.

SAN MATEO, CA--(Marketwired - Apr 24, 2017) - EverString, the leading provider of AI-assisted marketing and sales software, is proud to announce that it has been named to the JMP Securities Hot 100 List of the Best Privately Held Software Companies for 2017. EverString's SaaS platform helps B2B companies build pipeline and accelerate new customer acquisition using artificial intelligence and applied data science. The software research team of JMP Securities, one of the country's premier middle-market investment banks, publishes the annual "Hot 100" report to recognize leading private software companies based on their financial growth, product innovation, quality of leadership, customer traction and market potential. "We are honored to be named by JMP Securities as a Hot 100 software company for a second time," said J.J. Kardwell, CEO and Co-founder of EverString. "During the past year we have released major product innovations to help B2B marketing and sales professionals outperform. Our platform provides users with true self-service access to the power of artificial intelligence, comprehensive data, and predictive marketing and sales."

Understanding how different factors affect the supply chain remains a top priority for research firms around the globe. This unwavering drive represents the continued interest in advancing today's capabilities with state-of-the-art technology and adaptability. From artificial intelligence to refocusing on procurement, the state of supply chain continued to explode throughout 2016, and you need to understand why. Artificial intelligence (AI) is among the most well-recognized ideas in science fiction. However, it's true applications are becoming more apparent daily.

According to the researchers, the experiment shows how'fragile' current approaches are, as the exploit relies on barely perceptible'noise' to remove only a particular group from the image, making the attack far less obvious even to humans'If an adversary's objective is to remove all occurrences of a specific class (e.g. an adversary trying to hide all pedestrians to deceive an emergency braking system) then the attack is maximally inconspicuous if it leaves the prediction for all other classes unchanged and only hides the target class,' the authors explain. In their experiment, the researchers applied the universal adversarial perturbations to images in Cityscapes, a dataset that contains 3,475 images from 44 different cities. By doing this, they were able to remove nearly all pedestrian pixels – and, the background remained mostly unchanged. Such an attack would mean that pedestrians would be invisible to a self-driving car.

Apart from fears of mass unemployment, accidents with machinery, restrictions on freedom, increased economic inequality and a devalued human experience, the public are broadly optimistic about the arrival of artificial intelligence, according to one of the first surveys of British opinions about the technology. Research by the polling firm Ipsos Mori found nearly a third of people believe the risks of "machine learning" outweigh the benefits, while 36% believe the risks and benefits are balanced. Machine learning is technology that underpins internet searches, recommendations on Amazon and Netflix, and voice recognition on smartphones. The findings provide a snapshot of UK views on what some researchers regard as the early stages of a major revolution that is poised to affect almost every aspect of life. The research suggests that – while people are generally positive about the technology for improving medical treatments, guiding driverless cars and personalising education – substantial concerns remain.

Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems - that are in general NP-hard - efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; and (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact, given an instance of the problem at hand, even decompositions of the same width might yield extremely diverging runtimes. We thus propose here a novel and general method that is based on selection of the best decomposition from an available pool of heuristically generated ones. For this purpose, we require machine learning techniques that provide automated selection based on features of the decomposition rather than on the actual problem instance. Thus, one main contribution of this work is to propose novel features for tree decompositions. Moreover, we report on extensive experiments in different problem domains which show a significant speedup when choosing the tree decomposition according to this concept over simply using an arbitrary one of the same width.

This paper considers the problem of decentralized optimization with a composite objective containing smooth and non-smooth terms. To solve the problem, a proximal-gradient scheme is studied. Specifically, the smooth and nonsmooth terms are dealt with by gradient update and proximal update, respectively. The studied algorithm is closely related to a previous decentralized optimization algorithm, PG-EXTRA [37], but has a few advantages. First of all, in our new scheme, agents use uncoordinated step-sizes and the stable upper bounds on step-sizes are independent from network topology. The step-sizes depend on local objective functions, and they can be as large as that of the gradient descent. Secondly, for the special case without non-smooth terms, linear convergence can be achieved under the strong convexity assumption. The dependence of the convergence rate on the objective functions and the network are separated, and the convergence rate of our new scheme is as good as one of the two convergence rates that match the typical rates for the general gradient descent and the consensus averaging. We also provide some numerical experiments to demonstrate the efficacy of the introduced algorithms and validate our theoretical discoveries.

The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining tool-kit ProM (http://promtools.org). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.