Machine based text comprehension has always been a significant research field in natural language processing. Once a full understanding of the text context and semantics is achieved, a deep learning model can be trained to solve a large subset of tasks, e.g. text summarization, classification and question answering. In this paper we focus on the question answering problem, specifically the multiple choice type of questions. We develop a model based on BERT, a state-of-the-art transformer network. Moreover, we alleviate the ability of BERT to support large text corpus by extracting the highest influence sentences through a semantic similarity model. Evaluations of our proposed model demonstrate that it outperforms the leading models in the MovieQA challenge and we are currently ranked first in the leader board with test accuracy of 87.79%. Finally, we discuss the model shortcomings and suggest possible improvements to overcome these limitations.

The Assistant feed has been available since March and continues to add new capabilities. This true Assistant successor to the original Google Now is now offering more Snapshot audio suggestions, including YouTube Music, and sports results. Back in June, Assistant Snapshot picked up a "Start listening for a fresh morning" card. This was solely aimed at offering "Podcasts for you." That card is now called "Perk up with fresh audio picks" to offer "News, podcasts, and music."

In this video we continue on the topic of Lipschitz continuity by presenting a paper which proposes a projection method to enforce it! If you enjoy this video consider watching others which I have on the topic! Abstract: We investigate the effect of explicitly enforcing the Lipschitz continuity of neural networks with respect to their inputs. To this end, we provide a simple technique for computing an upper bound to the Lipschitz constant---for multiple p-norms---of a feed forward neural network composed of commonly used layer types. Our technique is then used to formulate training a neural network with a bounded Lipschitz constant as a constrained optimisation problem that can be solved using projected stochastic gradient methods.

Hollywood movies usually depict the suburbs as a place of conformity and dark secrets. Horror author Grady Hendrix says this is particularly true of 1980s films like Poltergeist and A Nightmare on Elm Street, which critique the idea of the suburbs as being clean and new. "I think Poltergeist and Nightmare on Elm Street are both movies that say, 'No, history doesn't begin where you say it begins. There are crimes in the past that have been buried,'" Hendrix says in Episode 428 of the Geek's Guide to the Galaxy podcast. Science fiction professor Lisa Yaszek says that suburban life has always been a particular source of anxiety for women. "I know from my own research that in the 1950s, women who were writing science fiction, absolutely one of their favorite topics was the horror of suburban life for women," she says.

That as a mind-set gets people narrowed. Netflix's core competency in data science enables the personalization of the streaming experience based on user behavior. Netflix classifies and tags content to get a nuanced view of consumer preferences. Netflix has developed over 1,000 tag types that classify content by genre, time period, plot conclusiveness, mood, etc. These tags help to define micro-genres, which, by 2014, had already reached 76,897.

Emilia Díaz-Struck is research editor and Latin American coordinator for the International Consortium of Investigative Journalists (ICIJ). She oversees data projects and has been involved in some major cross-border investigations including the Panama Papers, the Paradise Papers and the Offshore Leaks. The ICIJ receives vast amounts of files from whistleblowers and uses AI-powered technologies to sift through that data more efficiently. For our interview series with women working on the intersection of AI and journalism, Emilia spoke to us about how exactly AI is deployed and what impact it will have on investigative journalism. JournalismAI: You have a very diverse background in journalism, having worked with major organisations such as The Washington Post, the Press and Society Institute of Venezuela and co-founding your own news site Armando.info. How did you initially move into a data-driven role?

The subset of artificial intelligence known as machine learning is proving to be an essential part of our toolkit for understanding and dealing with one …

Netsurion will be incorporating Deep Instinct's deep learning endpoint security solution into its EventTracker threat protection platform.

As recommender systems send a massive amount of content to keep users engaged, users may experience fatigue which is contributed by 1) an overexposure to irrelevant content, 2) boredom from seeing too many similar recommendations. To address this problem, we consider an online learning setting where a platform learns a policy to recommend content that takes user fatigue into account. We propose an extension of the Dependent Click Model (DCM) to describe users' behavior. We stipulate that for each piece of content, its attractiveness to a user depends on its intrinsic relevance and a discount factor which measures how many similar contents have been shown. Users view the recommended content sequentially and click on the ones that they find attractive. Users may leave the platform at any time, and the probability of exiting is higher when they do not like the content. Based on user's feedback, the platform learns the relevance of the underlying content as well as the discounting effect due to content fatigue. We refer to this learning task as "fatigue-aware DCM Bandit" problem. We consider two learning scenarios depending on whether the discounting effect is known. For each scenario, we propose a learning algorithm which simultaneously explores and exploits, and characterize its regret bound.

Let's say for the sake of argument you're stuck at home for a long time watching too much of the stuff we euphemistically call "streaming content," by which I mean movies and TV. Come up with your own reason -- anything from being one of Japan's pathologically introverted hikikomori to, say, hiding out from some sort of potentially lethal respiratory virus. In any case, you will at some point sour on all the available programming options and scroll glumly through all the familiar title selection menus until you give up. Tiger King is more of a punch line than a TV show at this point, and, sure, you could plumb the depths of history's most creative auteurs over on the Criterion Channel, but that sounds hard, and if you are like me, you consider reading the morning news emotional labor. But what if there were a movie streaming service with no downsides?