Machines have already started their march towards artificial intelligence. Deep Learning and Neural Networks are probably the hottest topics in machine learning research today. Companies like Google, Facebook and Baidu are heavily investing into this field of research. Researchers believe that machine learning will highly influence human life in near future. Human tasks will be automated using robots with negligible margin of error. I'm sure many of us would never have imagined such gigantic power of machine learning.

When new trends and technologies burst onto the marketing scene, there's always a frantic effort to either keep up or provide guidance, especially when serious amounts of money are involved. It happened with social media, it happened with personalisation and big data, and it's happening now with artificial intelligence. We're approaching the top of the hype cycle where, like teenage sex, everyone is talking about it but very few are actually doing it. Conditions are perfect for the snake oil salesmen to move in. But there's real substance behind some of work being done in this field, and in this post I'll try to navigate through the fog of rhetoric to understand what's required to make the most of the significant opportunities.

One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. "I really feel like I missed the mark in giving the correct amount of feedback," he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. "Unfortunately, there is not a way to edit submitted feedback," wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer.

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.

The robot will take its exams in a closed room without anyone else present, except for proctors and a notary.

DATAVERSITY Education, LLC announced the agenda and opened registration for the company's newest online conference, Smart Data Online (SDO). The event will be held online at smartdataweek.com on July 13th, 2016 from 8:00 am to 2:20 pm Pacific Time. Registration is free and attendees will receive access to the on demand recordings, slides, and materials following the event. SDO is the newest event to be added to DATAVERSITY's educational programs, and is designed to provide guidance on executing and implementing a successful data strategy using new technologies in the fields of machine learning, cognitive computing, and artificial intelligence. Throughout the day on July 13th there will be six, 40-minute presentations, each followed by a 10 minute "Q & A" discussion with the presenter(s).

SeaDrone, the underwater robot coming out of a new company founded by two Stanford AI lab veterans, is aiming to make fish farming a lot easier--particularly for smaller aquaculture operations--by making underwater inspection cheaper and easier. The ocean ROV's story is not an unusual one for Silicon Valley: two Stanford students meet over a lab bench, get an idea that something they'd been tinkering around with for their themselves could be turned into a product and the basis of a company. It's a story Silicon Valley loves. Eduardo Moreno met Shuyun Chung in the Stanford AI lab in 2013. Moreno, in the thick of his studies for a master's degree in mechanical engineering, was working on underwater robot hardware in collaboration with King Abdullah University of Science and Technology in Saudi Arabia.

There are some human attributes robots could never replace - or at least that's what you might hope. But one university has brought that into question by replacing one of their teaching assistants with a machine. Student Tyson Bailey began to wonder if Jill was a computer and posted his suspicions on Piazza. 'We were taking an AI course, so I had to imagine that it was possible there might be an AI lurking around,' said Bailey, who lives in Albuquerque, New Mexico. 'Then again, I asked Dr. Goel if he was a computer in one of my first email interactions with him.

Men who are not tall, dark and handsome may want to brush up on their storytelling skills. A new study has revealed males who can spin a good yarn are seen as more attractive to women. Females also perceive strong storytellers as being more important and of higher status in society. A new study has revealed males who spin a good yarn (illustrated by a stock image) are more attractive to women. Researchers from the University of North Carolina at Chapel Hill and the University at Buffalo (SUNY) asked 388 students, 55 per cent of whom were women, to rate the attractiveness of a potential partner based on a written description – much as they might on an online dating site.

On the Internet, "nobody knows you're a dog," as the old meme goes, and today, the same can increasingly be said of robots. There are already scheduling robots that are virtually indistinguishable from humans, and recently students at the Georgia Institute of Technology learned that "Jill Watson" -- a teaching assistant they had relied upon all semester -- was in fact artificially intelligent. "The world is full of online classes, and they're plagued with low retention rates," said Ashok Goel, a Georgia Tech professor who teaches a class entitled Knowledge-Based Artificial Intelligence. "One of the main reasons many students drop out is because they don't receive enough teaching support. We created Jill as a way to provide faster answers and feedback."