WHAT: SoftServe's "Will Artificial Intelligence Change Healthcare?" on-demand webinar will deep dive into the most talked about topic in Healthcare – Artificial Intelligence. WHO: During the webinar SoftServe's Eugene Borukhovich, Senior Vice President and Healthcare Global Vertical Practice Leader, will provide insights on how AI can add value in Healthcare by: WHERE: Register to access a recording of the "Will Artificial Intelligence Change Healthcare?" webinar. Eugene Borukhovich is an international expert on healthcare information technology innovation. He is also a member of HIMSS EU Industry Advisory Committee, convened in September 2014 to discuss and collaborate on key Healthcare IT topics. Eugene is a frequent speaker at various healthcare conferences and events, including HealthXL, mHealth Summit, Health 2.0, Week of Health and INNovation, etc. Eugene's articles and blogs have been published in numerous healthcare resources including SoftServe United, HealthWorksCollective, Medical Design Technology, intrepidNOW, and many more.

Imagine discovering someone you thought was human is, in fact, a robot. It sounds like the stuff of science fiction. But that's what happened to a class full of Georgia Tech students recently, when they learned that "Jill," their teaching assistant, was actually a piece of software. CBC Radio technology columnist Dan Misener explains what happened. The story starts with a computer science professor named Ashok Goel, who teaches at the Georgia Institute of Technology.

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1) opacity as intentional corporate or state secrecy, (2) opacity as technical illiteracy, and (3) an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented), and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These are just some examples of mechanisms of classification that the personal and trace data we generate is subject to every day in network-connected, advanced capitalist societies. These mechanisms of classification all frequently rely on computational algorithms, and lately on machine learning algorithms to do this work. Opacity seems to be at the very heart of new concerns about'algorithms' among legal scholars and social scientists. The algorithms in question operate on data. Using this data as input, they produce an output; specifically, a classification (i.e. They are opaque in the sense that if one is a recipient of the output of the algorithm (the classification decision), rarely does one have any concrete sense of how or why a particular classification has been arrived at from inputs.

Artificial neural networks are a fascinating area of study, although they can be intimidating when just getting started. There are a lot of specialized terminology used when describing the data structures and algorithms used in the field. In this post you will get a crash course in the terminology and processes used in the field of multi-layer perceptron artificial neural networks. Crash Course In Neural Networks Photo by Joe Stump, some rights reserved. We are going to cover a lot of ground very quickly in this post.

R is probably every data scientist's preferred programming language (besides Python and SAS) to build prototypes, visualize data, or run analyses on data sets. There are so many libraries, applications and techniques exist to explore data in R that I'm sure even experts don't know them all! Aspiring data scientists who are reading this though, fear not, for you are well on your way to understanding these secrets. The links provide the ability to download the pdfs of the books. Authored by: Trevor Hastie and Rob Tibshirani, recognized Stanford professors and authors of "The Elements of Statistical Learning" What you'll learn: Implementation of statistical and machine learning techniques in R This book will teach you what you need to know, without harassing you much about the math behind it all.

If robots are going to take over the world, they could at least have the courtesy not to bump into us while they're at it. That's not as easy as it sounds, though, especially when a robot is trying to make its way through a bustling space like a mall, hospital, or crowded city street. Thankfully, researchers have developed an algorithm that could give robots the ability to deftly maneuver through spaces packed with unpredictable humans. Robots are gradually leaving controlled spaces like labs and factories and edging into more settings in which they will inevitably encounter human beings (see "Are You Ready for a Robot Colleague?"). We navigate hectic spaces by reading other people's movements and planning our paths accordingly.

Continuing the tradition of Machine Learning Summer Schools, this year's summer school is going to be held at the University of Cádiz, in Cádiz, Spain, from 11-21th May, 2016. The MLSS is a course offered to graduate students, researchers and professionals. The courses consists of lectures by respected researchers who come from the industry as well as from academia. The lectures will touch several fundamental as well as advanced concepts related to, but not limited to, machine learning, data analysis and inference. There will also be tutorials, which will concentrate on the practical aspect of machine learning.

Bill Gates speaks during the Forbes' 2015 Philanthropy Summit Awards Dinner on June 3, 2015 in New York City. Many are still wary about the effects of future artificial intelligence in humanity. But Microsoft cofounder and the world's richest man Bill Gates says AI won't be a threat, instead it will be "extremely helpful" in managing human lives. The actively evolving field of artificial intelligence has revolutionized the healthcare, business and education sectors around the world. AI also continues to prove its ubiquity by making great advances in technology and robotics but many warn about the existential risks of artificial intelligence.

Of course, if you are an experienced Python programmer you will be able to skip this step. Even if so, I suggest keeping the very readable Python documentation handy. KDnuggets' own Zachary Lipton has pointed out that there is a lot of variation in what people consider a "data scientist." This actually is a reflection of the field of machine learning, since much of what data scientists do involves using machine learning algorithms to varying degrees. Is itnecessary to intimately understand kernel methods in order to efficiently create and gain insight from a support vector machine model?

Regression is still one of the most widely used predictive methods. If you are unfamiliar with Linear Regression, check out my: Linear Regression using Excel lesson. It will explain the more of the math behind what we are doing here. This lesson is focused more on how to code it in Python. What we have is a data set representing years worked at a company and salary.