Ashok Goel wants to expand high-quality education to "millions" more people over the internet. It's the same goal that's pushed universities to make more and more courses and degree programs available over the internet, making it possible for students living on the far sides of the word to get degrees from American universities -- and vice versa. But online education has a problem: Of the hordes of students that sign up for massive open online classes (MOOCs), an average of less than 7% finish. Goel thinks artificial intelligence can change that. "There are many reasons" students don't finish, he told Tech Insider.

For decades, science fiction authors, futurists, and movie makers alike have been predicting the amazing (and sometimes catastrophic) changes that will arise with the advent of widespread artificial intelligence. So far, AI hasn't made any such crazy waves, and in many ways has quietly become ubiquitous in numerous aspects of our daily lives. From the intelligent sensors that help us take perfect pictures, to the automatic parking features in cars, to the sometimes frustrating personal assistants in smartphones, artificial intelligence of one kind of another is all around us, all the time. While we've yet to create self-aware robots like those that pepper popular movies like 2001: A Space Odyssey and Star Wars, we have made smart and often significant use of AI technology in a wide range of applications that, while not as mind-blowing as androids, still change our day-to-day lives. One place where artificial intelligence is poised to make big changes (and in some cases already is) is in education.

Watch this webinar, presented by Kris Hammond, Chief Scientist of Narrative Science, to learn about the different subfields of technologies that fall under the umbrella of AI such as machine learning, advanced analytics, and advanced natural language generation. Viewers will finish the webinar understanding how the different AI technologies emulate human reasoning and how they may be able to apply these technologies to their own business.

Google is holding its biggest event of the year, showing off everything that it's going to release through 2016. The general public and developers will get their first glimpse at the products and technology that's going to rule our lives – via our phone, home, car, computer and everything else. Some of what will be announced has already leaked, and others have been spoiled by Google themselves in advance of the event. Those include rumours about a new device for the home and new technology for your phone, as well as Google's update to Android Pay that now allows it to be used in the UK. But the main event is still set to be packed with surprises, including the first details of Google's own virtual reality headset and new phone technology.

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.

Human communication takes many forms, including speech, text and instructional videos. It typically has an underlying structure, with a starting point, ending, and certain objective steps between them. In this paper, we consider instructional videos where there are tens of millions of them on the Internet. We propose a method for parsing a video into such semantic steps in an unsupervised way. Our method is capable of providing a semantic "storyline" of the video composed of its objective steps. We accomplish this using both visual and language cues in a joint generative model. Our method can also provide a textual description for each of the identified semantic steps and video segments. We evaluate our method on a large number of complex YouTube videos and show that our method discovers semantically correct instructions for a variety of tasks.

One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.

Twitter is an extremely popular online social networking and micro-blogging service. Users communicate through "tweets" - these are short 140-character messages or opinions about different topics. This site is a mine of information about users and their interests - their profile, views, attitudes, observations, people they follow on the site, etc. Apart from being used as a channel of communications between family and friends, Twitter is also used for real-time news updates, recommendations and sharing content. Processing all this information will provide marketers and opinion leaders with a wealth of knowledge about consumers and their behavior and enable them to design effective marketing strategies. Join this webinar to learn how to extract, analyse and utilize this data by implementing machine learning algorithm on the available information.

Everyone knows about the giant skills gap that is haunting the IT sector worldwide. According to IoT company PTC, it is estimated that in the next ten years more than two million IT and communication jobs will be unfulfilled. To address this, several universities have come up with degrees that address the different skills needed in the IoT market, including user interfaces, networks, artificial intelligence, networking, and others. CBR lists ten courses being taught in the UK institutions. Offering both a full time or part time (12 and 24 months respectively) course, University of London's Royal Holloway has built a degree based on computer science, technology and engineering.

This is a compilation has everything you need to jumpstart your skills in the core tasks of data transformation, modeling, and visualization. MODELING Below is a list of popular analysis from Rexer's 2013 survey. The table is biased towards customer transaction, text, and social media data. CRAN has pages dedicated to each typical task of statistical computing http://cran.r-project.org/web/views/ Python has several packages tailored for statistical analysis including Pandas, Orange, PyBrain and Scikit-learn TRANSFORMATION OpenRefine is designed to help journalists and other non technical people organize incomplete data from different sources.