Futurists envision a doomsday scenario where robots rise up against us. But Artificial Intelligence and robots are not the same thing, and Artificial Intelligence software has quietly crept into many facets of our lives. Artificial Intelligence is used in computer games and in the software that helps us parallel park. Artificial Intelligence is about designing intelligent software that can analyze its environment and make intelligent choices for online learning. But what exactly could be the future of Artificial Intelligence in eLearning?

Deep Learning: Intelligence from Big Data Tue Sep 16, 2014 6:00 pm - 8:30 pm Stanford Graduate School of Business Knight Management Center – Cemex Auditorium 641 Knight Way, Stanford, CA A machine learning approach inspired by the human brain, Deep Learning is taking many industries by storm. Empowered by the latest generation of commodity computing, Deep Learning begins to derive significant value from Big Data. It has already radically improved the computer's ability to recognize speech and identify objects in images, two fundamental hallmarks of human intelligence. Industry giants such as Google, Facebook, and Baidu have acquired most of the dominant players in this space to improve their product offerings. At the same time, startup entrepreneurs are creating a new paradigm, Intelligence as a Service, by providing APIs that democratize access to Deep Learning algorithms.

This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.

Editor's note: Digital Trends has partnered with WebVisions, the internationally recognized design, technology and user-experience conference, to help bring luminary Douglas Rushkoff to this year's event in Portland, Oregon. As part of our partnership, we're also pleased to feature select content from WebVisions' insightful blog, Word. This week, contributor Mark Wyner wonders how we go about teaching artificial intelligence right from wrong. Twitter has admitted that as many as 23 million (8.5 percent) of its user accounts are autonomous Twitterbots. Many are there to increase productivity, conduct research, or even have some fun.

In just our fourth session together, Steve was already beginning to sound discouraged. It was Thursday of the first week of an experiment that I had expected to last for two or three months, but from what Steve was telling me, it might not make much sense to go on. "There appears to be a limit for me somewhere around eight or nine digits," he told me, his words captured by the tape recorder that ran throughout each of our sessions. "With nine digits especially, it's very difficult to get regardless of what pattern I use--you know, my own kind of strategies. It really doesn't matter what I use--it seems very difficult to get." Steve, an undergraduate at Carnegie Mellon University, where I was teaching at the time, had been hired to come in several times a week and work on a simple task: memorizing strings of numbers. I would read him a series of digits at a rate of about one per second--"Seven ... four ... zero ... one ... one ... nine ..." and so on--and Steve would try to remember them all and repeat them back to me once I was done. One goal was simply to see how much Steve could improve with practice. Now, after four of the hour-long sessions, he could reliably recall seven-digit strings--the length of a local phone number--and he usually got the eight-digit strings right, but nine digits was hit or miss, and he had never managed to remember a 10-digit string at all. And at this point, given his frustrating experience over the first few sessions, he was pretty sure that he wasn't going to get any better. What Steve didn't know--but I did--was that pretty much all of psychological science at the time indicated that he was right. Decades of research had shown that there is a strict limit to the number of items that a person can retain in short-term memory, which is the type of memory the brain uses to hold on to small amounts of information for a brief period of time. If a friend gives you his address, it is your short-term memory that holds on to it just long enough to write it down. Or if you're multiplying a couple of two-digit numbers in your head, your short-term memory is where you keep track of all the intermediate pieces: "Let's see: 14 times 27 ... First, 4 times 7 is 28, so keep the 8 and carry the 2, then 4 times 2 is 8 ..." and so on.

Successful machine learning researchers are identified in elementary school machine learning competitions. Only the most creative, innovative, and gifted students are selected. If you were never aware of the process, then it means that you failed in the secret initial qualifiers, and weren't even close to earning a place in the program. This process may sound harsh, but it would simply be cruel to try to train someone in the art of machine learning if they don't possess the raw talent.

From a huge effort to help kids realize their potential to a celebration of our dear old planet, this week brought plenty of interesting and inspiring news around Microsoft. We've rounded up some of the highlights in this latest edition of Weekend Reading. Earlier this week, Microsoft announced grants to 100 nonprofit partners in 55 countries as part of YouthSpark, a global initiative to increase access for young people to learn computer science. In turn, these nonprofit partners -- such as Laboratoria, CoderDojo and City Year -- will use the power of local schools, businesses and community organizations to empower students to achieve more for themselves, their families and their communities. The nonprofits will build upon the work that Microsoft already has underway through programs like Hour of Code with Code.org,

We've put together an introductory lecture on TensorFlow as part of CS 224D, Stanford's deep-learning for NLP class. As far as we can tell, this is one of the first academic lectures on TensorFlow (aside from Google's official docs of course). We hope it'll prove useful to the ML community. Feel free to ask us questions on this thread, and we'll answer to the best of our ability.

Summary: In which we attempt to answer the question, how does someone in school or recently out enter the exciting world of data science. There is no question that comes up more frequently than'how do I become a data scientist'. I've actually written several articles on this topic (and will reference them liberally in this post) but they lacked the global perspective that potential new entrants to data science want. I'm going to try to resolve here. I thought about changing the title to "Doing Data Science" instead of becoming a Data Scientist to focus on the activity and not just the job title.

R is a purpose-built language meant for statistical computing, and is a clear winner for large-scale data-mining, visualization and reporting. You have easy access to a huge collection of packages (through the CRAN repository) that enable you to apply almost all kinds of Machine Learning algorithms, statistical tests and analysis procedures. The language itself has an elegant--albeit esoteric--syntax for expressing relationships, transforming data and performing parallelized operations.