Learning visual feature representations for video analysis is a daunting task that requires a large amount of training samples and a proper generalization framework. Many of the current state of the art methods for video captioning and movie description rely on simple encoding mechanisms through recurrent neural networks to encode temporal visual information extracted from video data. In this paper, we introduce a novel multitask encoder-decoder framework for automatic semantic description and captioning of video sequences. In contrast to current approaches, our method relies on distinct decoders that train a visual encoder in a multitask fashion. Our system does not depend solely on multiple labels and allows for a lack of training data working even with datasets where only one single annotation is viable per video. Our method shows improved performance over current state of the art methods in several metrics on multi-caption and single-caption datasets. To the best of our knowledge, our method is the first method to use a multi-task approach for encoding video features. Our method demonstrates its robustness on the Large Scale Movie Description Challenge (LSMDC) 2017 where our method won the movie description task and its results were ranked among other competitors as the most helpful for the visually impaired.

Conversational agents are gaining popularity with the increasing ubiquity of smart devices. However, training agents in a data driven manner is challenging due to a lack of suitable corpora. This paper presents a novel method for gathering topical, unstructured conversational data in an efficient way: self-dialogues through crowd-sourcing. Alongside this paper, we include a corpus of 3.6 million words across 23 topics. We argue the utility of the corpus by comparing self-dialogues with standard two-party conversations as well as data from other corpora.

From the ancient Greeks to Shakespeare to Hollywood, humans have attempted to understand their fellow man through labeling and categorization. There was Hippocrates's blood, phlegm, yellow and black bile; the classic dramatic archetypes of hero, ingenue, jester and wise man; and, of course, Carrie, Charlotte, Samantha and Miranda from the famous HBO series More rigorously, psychologists have worked to develop empirical tests that assess core aspects of personality. The "Big Five" traits (extroversion, neuroticism, openness, conscientiousness and agreeableness) emerged in the 1940s through studies of the English language for descriptive terms. Those categories were validated in the 1990s as a scientifically backed way to evaluate a person's character. Through a series of questions, researchers learn whether you are high, low, or in between in each one of those qualities.

Did AI write this article?Depositphotos enhanced by CogWorld Imagine how productive Woodward and Bernstein might have been if only they had robots to write their articles for the Washington Post. With a little A.I. on their side, they might have taken down Nixon in days instead of years. "A lot of people don't realize this, but a lot of the news stories you read now are increasingly written by artificial intelligence," said Stephen Ibaraki, social entrepreneur, futurist and chair at REDDS Capital in an interview for my upcoming book, Uber Yourself Before You Get Kodaked: A Modern Primer on A.I. for the Modern Business, co-authored by Michael Ashley. "You get these news releases about things that are happening in sports, for example, or in business. But people are not creating these pieces anymore. Lots of us are spending hours on our mobile phones reading updates about events and news flashes never realizing it's A.I. that's generating this stuff now."

A fascinating project called Amadeus Code promises to out-Tay-Tay Tay Tay and out-Bon Bon Iver. The AI-based system uses data from previous musical hits to create entirely new compositions on the fly -- and darn if these crazy robot-songs aren't pretty good. The app, which is available from the iTunes Store but doesn't seem to be working properly, creates song sketches in minutes, freeing you up to create beautiful lyrics and a bit of accordion accompaniment. The video above is a MIDI version of an AI-produced song and the video below shows the song full-produced using non-AI human musicians. The results, while a little odd, are very impressive.

Sea mines remain a real and terrifying threat and are a central part of many countries' plans to disrupt maritime operations in the event of a conflict.

AI is already beginning to catch up to Human Levels, with the power of Helios Neural Network fuelling it. Leading to better patient outcomes and reduced costs for healthcare providers.