NEW YORK, March 25, 2016 /PRNewswire-iReach/ -- Today at 9:00 am EST, exClone Inc. announced the release of the Debate Guide Version 2.0 as a tool offered by the Wall Street Journal to its audience following the 2016 presidential elections. The Debate Guide brings direct quotes from what was discussed by the presidential candidates during the primetime debates, and the system is updated after each new debate. The tool allows users to compare the candidates for each issue discussed and brings quotes to particular questions through a dialogue, chatbot interface. Debate Guide 2.0 offers enhanced features that build on the success of the earlier beta versions. The Debate Guide's dialogue flow is a prime example to how advanced chatbots can handle content, knowledge, and expertise.

Artificial intelligence is advancing rapidly and poised to change the status quo in any number of industries, including healthcare. A recent report by Frost & Sullivan predicts the AI market in healthcare will reach 6 billion by 2021, up from just 600 million two years ago. With the shift to a value-based reimbursement model, ushered in with the Affordable Care Act, hospitals and providers are looking for new ways to increase efficiencies and improve patient outcomes. Cognitive solutions such as IBM's Watson system can assess huge amounts of patient data, provide guidance and decision support, and improve clinical workflow. The goal is to support the physician, not replace him or her, said Anil Jain, vice president of IBM's Watson Health and an internist and medical informatics specialist at the Cleveland Clinic.

In our latest inspection of Github repositories, we focus on "data science" projects. Unlike other searches we have performed over the past several months, nearly all of the repositories which show up (listed by number of stars* in descending order) are resources for learning data science, as opposed to tools for doing. As such, this is much less a software listing than it is a collection of tutorials and educational resources. There are, however, a few software surprises in here as well, such as a data science-oriented IDE and a great notebook-related project. We include, however, the standard informational notification we have placed on our previous Github Top 10 lists: open source tools have been used by 73% of data scientists in the past 12 months, according to a recent KDnuggets survey (and accounting for the 12 months prior to the survey). While the following repositories focus mainly on learning resources, previous offerings have been software-heavy; also, open source learning materials are the new black, and a main source of learning for data scientists these days.

Just by typing the letters "A," "r-o-b," "w-r-o," "t-h-i," and "s-e-n" into the text messenger on a mobile phone, the predictive text function helped write that first sentence. What will do so is software such as StatsMonkey, which can automate sports reporting. The software analyzes statistics from a baseball game and "generates natural language text" to come up with phrases such as "Things looked bleak for the Angels when they trailed by two runs in the ninth inning" and even includes quotes from players. This is just one of the many well-researched examples presented by Martin Ford in his scarily intriguing new book, "Rise of the Robots." Ford is not some Luddite scared of technology, though.

Microsoft said its researchers created Tay as an experiment to learn more about computers and human conversation. On its website, the company said the program was targeted to an audience of 18-to 24-year-olds and was "designed to engage and entertain people where they connect with each other online through casual and playful conversation".

We investigate the choice of tuning parameters for a Bayesian multi-level group lasso model developed for the joint analysis of neuroimaging and genetic data. The regression model we consider relates multivariate phenotypes consisting of brain summary measures (volumetric and cortical thickness values) to single nucleotide polymorphism (SNPs) data and imposes penalization at two nested levels, the first corresponding to genes and the second corresponding to SNPs. Associated with each level in the penalty is a tuning parameter which corresponds to a hyperparameter in the hierarchical Bayesian formulation. Following previous work on Bayesian lassos we consider the estimation of tuning parameters through either hierarchical Bayes based on hyperpriors and Gibbs sampling or through empirical Bayes based on maximizing the marginal likelihood using a Monte Carlo EM algorithm. For the specific model under consideration we find that these approaches can lead to severe overshrinkage of the regression parameter estimates in the high-dimensional setting or when the genetic effects are weak. We demonstrate these problems through simulation examples and study an approximation to the marginal likelihood which sheds light on the cause of this problem. We then suggest an alternative approach based on the widely applicable information criterion (WAIC), an asymptotic approximation to leave-one-out cross-validation that can be computed conveniently within an MCMC framework.

This article outlines a method for automatically generating models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. This is useful for designing empirically grounded agent-based simulations and for gaining direct insight into observed dynamic processes. We use an efficient model representation and a genetic algorithm-based estimation process to generate simple approximations that explain most of the structure of complex stochastic processes. This method, implemented in C++ and R, scales well to large data sets. We apply our methods to empirical data from human subjects game experiments and international relations. We also demonstrate the method's ability to recover known data-generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity.

Over July 4th weekend in 1981, several hundred game nerds gathered at a banquet hall in San Mateo, California. Personal computing was still in its infancy, and the tournament was decidedly low-tech. Each match played out on a rectangular table filled with paper game pieces, and a March Madness-style tournament bracket hung on the wall. The game was called Traveller Trillion Credit Squadron, a role-playing pastime of baroque complexity. Contestants did battle using vast fleets of imaginary warships, each player guided by an equally imaginary trillion-dollar budget and a set of rules that spanned several printed volumes. If they won, they advanced to the next round of war games--until only one fleet remained. Doug Lenat, then a 29-year-old computer science professor at nearby Stanford University, was among the players. But he didn't compete alone. He entered the tournament alongside Eurisko, the artificially intelligent system he built as part of his academic research. Eurisko ran on dozens of machines inside Xerox PARC--the computer research lab just down the road from Stanford that gave rise to the graphical user interface, the laser printer, and so many other technologies that would come to define the future of computing. That year, Lenat taught Eurisko to play Traveller. Doug Lenat says his common-sense engine is a new dawn for AI. The rest of the tech world doesn't really agree with him. Lenat fed the massive Traveller rulebook into the system and asked it to find the best way of winning.

As R programming language becoming popular more and more among data science group, industries, researchers, companies embracing R, going forward I will be writing posts on learning Data science using R. The tutorial course will include topics on data types of R, handling data using R, probability theory, Machine Learning, Supervised – unSupervised learning, Data Visualization using R, etc. Before going further, let's just see some stats and tidbits on data science and R.

This article was taken from The WIRED World in 2016 -- our fourth annual trends report, a standalone magazine in which our network of expert writers and influencers predicts what's coming next. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online. When assessing a patient, medics look at snapshots of physiological data that are manually taken by doctors or nurses, and make decisions against patient history, family background and test results, as well as their own knowledge and experience. But what if this data was constantly being taken, every second of every day? And what if a system was clever enough to compare these readings to thousands of patients worldwide with a similar history and disorder, as well as all the current clinical guidelines and studies, and make clinical suggestions to doctors?