This is the core of the event. This is where you will meet a lot of cool people. There will probably be beer and pizza.

Google confirmed they plan to improve Google Translate's accuracy through artificial intelligence called deep learning. Deep learning is an advanced model of machine learning where an algorithm takes what it has already "learned" (data previously processed) and uses it to form new ways to solve problems in a pattern. Jeff Dean, Google Senior Fellow, confirmed that his team has been working with the Google Translate team to "scale out experiments with translation based on deep learning." Deep learning and technologies derived from it, including deep and recurrent neural nets, are objectively excellent at tackling sequential problems such as speech and image recognition, as long as there is sufficient existing material to train them. On that front, Google Translate's vast data trove of translated material should indeed prove quite useful.

Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning. In this post you will discover the linear regression algorithm, how it works and how you can best use it in on your machine learning projects. You do not need to know any statistics or linear algebra to understand linear regression. This is a gentle high-level introduction to the technique to give you enough background to be able to use it effectively on your own problems. Linear Regression for Machine Learning Photo by Nicolas Raymond, some rights reserved.

Today, Facebook has apologised for a Safety Check error that led to people around the world being texted in the wake of the Sunday's bombing in Lahore, Japan's Hitomi X-ray satellite has lost communication with Earth, Microsoft has issued a formal explanation for the actions of its short-lived machine learning chatbot, Tay, and more. Get WIRED Awake sent straight to your inbox every weekday morning by 8am. Click here to sign up to the WIRED Awake newsletter. In the wake of a suicide bombing that left at least 69 people dead in the Pakistani city of Lahore on Sunday, Facebook has apologised for an error in its Safety Check disaster response system that saw people around the world being asked to check in as safe (The Guardian). Users in areas as geographically diverse as Australia, Egypt and Belgium received text messages asking if they'd been affected by the explosion, without any information on where the incident had occurred.

Two-thirds of Americans believe that, in 50 years, robots and computers will do much of the work humans now do. The World Economic Forum's 2016 report, The Future of Jobs, estimates that 5 million jobs will be lost to automation by 2020 and that the number will keep growing. Jobs that once seemed like "safe bets"--office workers and administrative personnel, manufacturing, and even law--will be hit hardest, the report estimates. "There are some overarching shifts poised to change the nature of work itself over the next decade," says Devin Fidler, research director at Institute for the Future, a nonprofit research center focused on long-term forecasting. So what do you need to work on to be marketable in 2025?

Almost a week after being shut down for spewing racist and sexist comments on Twitter, Microsoft Corp's artificial intelligence'chatbot' called Tay briefly rejoined Twitter on Wednesday only to launch a spam attack on its followers. "Tay remains offline while we make adjustments," a Microsoft representative said in an email. It was taken offline following the incident, according to a Microsoft representative, in an effort to make "adjustments" to the artificial intelligence profile. According to its Twitter profile, Tay is "an artificial intelligent chatbot developed by Microsoft's Technology and Research and Bing teams to experiment with and conduct research on conversational understanding."

At its 2016 developer conference, Microsoft showed off an incredible set of glasses powered by its AI that can tell a blind person what's going on around them.

Almost a week after being shut down for spewing racist and sexist comments on Twitter, Microsoft Corp's artificial intelligence'chatbot' called Tay briefly rejoined Twitter on Wednesday only to launch a spam attack on its followers. The incident marks another embarrassing setback for the software company as it tries to get ahead of Alphabet Inc's Google, Facebook Inc and other tech firms in the race to create virtual agents that can interact with people and learn from them. The TayTweets (@TayandYou) Twitter handle was made private and the chatbot stopped responding to comments Wednesday morning after it fired off the same tweet to many users. "You are too fast, please take a rest...," tweeted Tay to hundreds of Twitter profiles, according to screen images published by technology news website The Verge. Tay's Twitter account was accidentally turned back on while the company was fixing the problems that came to light last week, Microsoft said on Wednesday.

In "Teaching Data Science in English (Not in Math)", the Feb-08-2016 entry of his Web log, "The Datatist", Charles Givre criticizes the use of specialized math symbols (capital sigma for summation, etc.) and Greek letters as being confusing, especially to newcomers to the field. He suggests that "English" (pseudo-code) be used instead, such as the following: Although there are some flaws in this particular comparison (1. the first example includes an unnecessary middle portion, 2. it also features the linear model definition, while the pseudo-code example does not, and 3. the indexing variable in the summation is straightforward in this case, but is not always so), I tend to agree. Despite my own familiarity with the math jargon, I agree with him that, in many cases, pseudo-code is easier to understand. Pseudo-code is certainly simpler syntactically since it tends to make heavy use of (sometimes deeply-nested) functions, as opposed to floating subscripts, superscripts and baroque symbols. Pseudo-code often employs real names for parameters, variables and constants, as opposed to letters. It is also closer to computer code that one actually writes, which is the point of this post.

Machine learning is an artificial intelligence branch that has been well applied and recognized as an effective tool to handle a wide range of real situations. In the last few years, we have witnessed the explosion of Big Data, which has enables researchers to store data for analysis in an unprecedented way. This explosion in data available for analysis is as evident in healthcare as anywhere else. In particular, this special issue is focused on the areas of bioinformatics and biomedical engineering. These are two of the fastest developing research fields in the last few decades, since the biological data used to provide information is rapidly generated, and it is mandatory to be able to extract information and knowledge from them, as technological innovation in these fields is to be probably one of the most important developments in the next coming years.