Scheduled Sampling was at NIPS last year; the reviews are also online. This is actually the third time I've tried to make my way through this paper, and to force myself to not give up again, I'm writing my impressions here. Given that this paper is about two things I know a fair amount about (imitation learning and neural networks), I kept getting frustrated at how hard it was for me to actually understand what was going on and how it related to things we've known for a long time. So this post is trying to ease entry for anyone else in that position. What is the problem this paper is trying to solve?

Many AIs are developed to sift through and make sense of Big Data. But behind-the-scenes, others are acquiring softer human skills and deploying their algorithms to make art. On Monday, Hitoshi Matsubara, a professor of computer science from the Future University in Hakodate in northern Japan, announced that his research team's short-form novel--co-created with an AI--had passed the initial screening of a domestic literary competition. Though their creation didn't nab the grand prix, the human-machine collaboration showed the early promises of what could be, if the team's AI is refined in the future. "So far, AI programs have often been used to solve problems that have answers, such as Go and shogi," said Matsubara, in a report by the Yomiuri Shimbun.

Statistics.com, a provider of online education in statistics and analytics, announces a partnership with CrowdANALYTIX, a predictive modeling "managed crowdsourcing" company, offering a new online course, "Applied Predictive Analytics in partnership with CrowdANALYTIX", which will run from Oct. 11 to Nov 8, 2013. The goal of this course is to teach users (who have basic knowledge of R programming, predictive analytics and statistics) to apply machine learning techniques in real world case studies. This course provides a hands on approach, presenting the opportunity to participate in a private educational competition hosted by CrowdANALYTIX. Business Case Study: We will study data from the "daily deals" industry (consisting of websites like Groupon, Living Social etc. which source local deals to offer each day). The daily deals industry is emerging and highly competitive.

A new 300-page research report from Bank of America Merrill Lynch, Robot Revolution – Global Robot & AI Primer, and to a lesser extent BSG's report: Man and Machine in Industry 4.0, make for interesting reading and highlight the role of AI and the changing nature of jobs and work in the exponential growth of the robotics industry. "We are facing a paradigm shift which will change the way we live and work," the authors of the BoA ML report said. "The pace of disruptive technological innovation has gone from linear to parabolic in recent years. Penetration of robots and artificial intelligence has hit every industry sector, and has become an integral part of our daily lives." BoA ML report projects that the total global market for robots and artificial intelligence will reach 152.7 billion by 2020, and estimates that the adoption of these technologies could improve productivity by 30% in some industries.

Humans are nothing if not contrary. Technology destroying jobs is something most Americans accept will happen within their lifetimes, according to a new study by the Pew Research Center, just not to their own jobs -- which most believe won't change significantly in the next 50 years. Polling just over 2,000 Americans in June and July last summer to ask about their perception of the risk of jobs being automated, the researchers found a majority (65 per cent) of Americans believe that robots and/or software will "definitely" or "probably" be capable of doing much of the work that humans do now within 50 years' time. But when the robots and the algorithms move a little closer to home – and the question becomes specifically about the future security of their own jobs -- respondents' views are very different, with an even larger majority (80 per cent) convinced their own jobs and professions will remain largely unchanged and will exist in their current form 50 years from now. More than a third (36 per cent) of respondents expressed definitive confidence that their current job or occupation will "definitely" exist in its current form five decades from now vs just six per cent saying their current role will "definitely not" exist.

It was end of last year, I decided to research upon Machine learning (ML) and have been taking few little steps. I need to understand what it's all about ML and related hype factor that it has created in the technology industry. Few articles suggested that I should have good understanding of basic Mathematics, Statistics and few suggested that I need to be good in domain knowledge etc. etc. Most of the basic algorithms or ML Techniques has been there for many years but it has gained lot of momentum now. We see the modern systems have good computing power to execute ML at ease and also due to exponential data growth every year (Lot of data are available to us) which encourages us to build systems that could deliver better insights real-time.

Over the past few years we have witnessed an incredible explosion of interest and application of machine learning. Machine learning has become the predominant computational paradigm, and in short term it has gone from one successful application to another. However, machine learning is far from a unified field, and many different approaches and techniques are vying for primacy and dominance. Which raises an interesting question: is it possible to find a single all-purpose machine learning algorithm that can successfully tackle all protean problems that are currently being attacked from various angles. This search for this "Master Algorithm" in many respects has the flavor of the search for a unified field theory in Physics.

If you are one of the 40 million people who enjoy reading or writing the mostly romantic werewolf, superhero or historical fiction stories found on Canadian startup Wattpad, you may also be contributing to the development of the next generation of artificial intelligence. In a new paper called Augur: Mining Human Behaviors from Fiction to Power Interactive Systems, a group of Stanford University computer science researchers revealed that they used the Wattpad "corpus" – a collection of almost two billion words (or 600,000 chapters) written by regular people – to help a computer understand the world around it. The team intends to make the program they built, Augur, into an open-source tool that other researchers can build on. "The basic idea is that it's very difficult to program computers to understand the broad range of things that people do," says fourth-year PhD student Ethan Fast, co-author of the paper (published as part of the upcoming Computer Human Interaction conference) and a member of Stanford's Human-Computer Interaction Group. "Fiction has a lot of useful things to say about the world, and if you have enough of it, you can model it in much more depth than you could hope to manually."

If you have ever tried your hand at lip-reading in a noisy environment, you'll know it isn't easy. Now, researchers have invented a machine that can tell the difference between sounds that look the same on the lips to give anyone the ability to decipher what's being said. It is hoped the new technology could help people with hearing and speech impairments communicate more easily and even help solve crimes. Researchers have invented a machine that can tell the difference between sounds that look the same on the lips to give anyone the ability to decipher what's being said. The visual speech recognition technology, can be applied'any place where the audio isn't good enough to determine what people are saying,' according to Helen Bear, who created the machine alongside Richard Harvey at the University of East Anglia (UEA).

In San Francisco this week at Pier 48, overlooking the Giants' AT&T Ballpark, Google Cloud Platform (GCP) executives are holding a user conference to introduce products and services they hope will help make the case for choosing Google in the cloud. Sam Charrington, a cloud and big data analyst and advisor, summed up Google executives' pitch best this week on Twitter: "GCP exec team's operating thesis: 'Cloud's not done. MORE AT NETWORK WORLD: Is Google pushing the cloud envelope too far? Google is seen by many as being behind Amazon Web Services, Microsoft Azure and even IBM in the IaaS cloud market. In a new research note, Deutsche Bank investment analysts predicted that GCP is on a 400 million revenue run rate, which is roughly 20 times less than AWS's. But as cloud watcher Charrington notes: "Google came from behind to win in search, mail, maps, browser, mobile based on strength of tech/product.