At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. We recently asked some of the leading experts in Big Data, Data Science, Artificial Intelligence, and Machine Learning for their opinion on the most important developments of 2016 and key trends they 2017. "What were the main Big Data related events in 2016 and what key trends do you see in 2017?" We generally asked participants to keep their responses to within 100 words or so, but were amenable to longer answers if the situation warranted.

"We are using AI to improve healthcare experiences in a way that has never been done before," said Kyle Robertson, Founder and Chief Executive Officer, NarrativeDx. "Our partners have been eager to leverage our technology to better understand their patients' experiences. The demand we are seeing is phenomenal, and we are on track to increase our client base 10x in 2017." The NarrativeDx AI platform analyzes comments at a per-phrase level and identifies sentiment, named doctors and nurses, key themes, procedures, and even readmission risks from complex patient comments. The technology is proven to improve patient experience measures by 80 percent, and increase outpatient referrals by over $50 million.

Recently, I began a series on exploratory data analysis; so far, I have written about computing descriptive statistics and creating box plots in R for a univariate data set with missing values. Today, I will continue this series by analyzing the same data set with kernel density estimation, a useful non-parametric technique for visualizing the underlying distribution of a continuous variable.

The human brain is the most sophisticated organ in the human body. The things that the brain can do, and how it does them, have even inspired a model of artificial intelligence (AI). Now, a recent study published in the journal Frontiers in Systems Neuroscience shows how human intelligence may be a product of a basic algorithm. This algorithm is found in the Theory of Connectivity, a "relatively simple mathematical logic underlies our complex brain computations," according to researcher and author Joe Tsien, neuroscientist at the Medical College of Georgia at Augusta University, co-director of the Augusta University Brain and Behavior Discovery Institute and Georgia Research Alliance Eminent Scholar in Cognitive and Systems Neurobiology. He first proposed the theory in October 2015.

SYS-CON Events announced today that OpsGenie 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 2012, OpsGenie is an alerting and on-call management solution for dev and ops teams. OpsGenie provides the tools needed to design actionable alerts, manage on-call schedules and escalations, and ensure that the right people are notified at the right time, using multiple notification methods. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades.

Grammarly has your back -- and also $110 million in funding, after its first venture round. Grammarly uses artificial intelligence to help fix spelling, grammar and syntax mistakes. You can use it as a Chrome extension, in Microsoft Office or as a Windows app. Silicon Valley venture capitalist General Catalyst led the funding round, reports Bloomberg, and was joined by IVP, Spark Capital, Breyer Capital and SignalFire. Grammarly has around 6.9 million users and a $12-per-month premium option that corrects "complex writing issues."

Many experts called 2016 "the year of the chatbots." Thousands of chatbots already help businesses improve customer service, sell more, and increase earnings. This paper reports a Data Monsters overview of research on the best-known platforms for building chatbots. The relevance of this research is proved by the massive deployment of chatbots. Indeed, today chatbots are used to solve a number of business tasks across many industries like E-Commerce, Insurance, Banking, Healthcare, Finance, Legal, Telecom, Logistics, Retail, Auto, Leisure, Travel, Sports, Entertainment, Media and many others.

When did you last visit your primary care physician? During your appointment, the doctor placed a stethoscope over your chest, listening for whispers of abnormality in your heart beat. But most heart arrhythmias occur sporadically. Picture a world where your heart can be monitored continuously using a device you could purchase at a Best Buy or Target. Algorithms transform the raw data coming from your watch into diagnoses, and your doctor will be notified when a problem is detected.

Tech star personalities Stephen Hawkings, Elon Musk and Bill Gates warned the public about artificial intelligence (AI). The tech-oriented public and AI experts disagree, though, according to a recent research paper, "Tweeting AI: Perceptions of AI-Tweeters (AIT) vs Expert AI-Tweeters (EAIT)," (pdf) published by researchers at the School of Computing, Informatics and Decision Systems Engineering at the University of Arizona. "Co-occurring patterns tell us that AIT are in general fantasizing about the future whereas EAIT are grounded and realistic." Study authors used statistical analysis, sentiment analysis and machine learning to learn this insight and summarize the study with the conclusions below. Despite the overall negative sentiment of Twitter, overall the 2.3 million tweets analyzed about AI are positive by a large margin.

Thank you very much, as for your question, I meant that I have an univariate logistic regression model (i.e., with only one dependent binary variable), where the dependent variable must be explained by a number of binary independent variables (1,0). I have no problem when the independent variables are continuous in nature and normally distributed, because there is Hsieh (1998) who said that you can obtain the total sample size basing on the multiple correlation coefficient between Xi and the remaining predictors... However I didn't find anything like that for the model that I talked about above. So I hope to find in APPLIED LOGISTIC REGRESSION what I looking for.