Dealing with previously unseen slots is a challenging problem in a real-world multi-domain dialogue state tracking task. Other approaches rely on predefined mappings to generate candidate slot keys, as well as their associated values. This, however, may fail when the key, the value, or both, are not seen during training. To address this problem we introduce a neural network that leverages external knowledge bases (KBs) to better classify out-of-vocabulary slot keys and values. This network projects the slot into an attribute space derived from the KB, and, by leveraging similarities in this space, we propose candidate slot keys and values to the dialogue state tracker. We provide extensive experiments that demonstrate that our stratagem can improve upon a previous approach, which relies on predefined candidate mappings. In particular, we evaluate this approach by training a state-of-the-art model with candidates generated from our network, and obtained relative increases of 57.7% and 82.7% in F1 score and accuracy, respectively, for the aforementioned model, when compared to the current candidate generation strategy.

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.

From Fritz Lang's "Metropolis" in 1927 to the titanic "Terminator" franchise of present day, popular culture has reflected an unwavering fear of artificial intelligence (AI) and automation for decades. While the entertainment industry may have drawn inspiration from public anxiety over the past century, the broader reasons for aversion to digital evolution have changed. At the present, the more common fear of AI stems from concerns about displacement and annihilation of job roles across industries, as intelligent, AI-related technologies like process automation and digital content services continue to evolve and eliminate manual tasks that were once managed by human hands and minds. While this digital evolution may mean the end of certain roles in the future of work, for most it will more likely mean a simultaneous evolution of skills and responsibilities in human-based occupations. Forecasts indicate that all jobs will be impacted by AI and automation technologies in the future, but that doesn't mean those jobs will be replaced.

SAN FRANCISCO – When Liz O'Sullivan was hired at the New York City-based artificial intelligence company Clarifai in 2017, she felt lucky to find work at the intersection of two of her main interests: technology and ethics. Two years later, she found herself facing a moral dilemma. Clarifai was developing aerial photography and object detection tools as one of several companies working on Project Maven, a Pentagon drone surveillance program. After several conversations with friends and colleagues, O'Sullivan realized this type of technology eventually could be used for autonomous weapons. In January, she wrote to Clarifai CEO Matt Zeiler on behalf of a group of employees, seeking clarification on whether the technology would be used to create weapons and asking him to commit to a series of ethical measures.

Thanks for the article... which doesn't tell us why (and begs the question). At least it wasn't a mile long like some celebrity deaths which go on and on but never mention the cause of death (which everyone is fucking wondering about). Don't bother writing articles like this until you actually have something worth writing about.

And to demonstrate exactly how absurd the problem is, a new AI called Grover, developed by the Allen Institute for Artificial Intelligence and the University of Washington (and spotted by AI Weirdness), allows you to enter a headline, and it will generate hundreds of words of convincing, fake text that looks like it belongs in the New York Times or on CNN. Luckily for you, the researchers put Grover's fake news generator online for anyone to try. And let me say, from personal experience, that it can create some doozies. For a test, I wrote the headline: "Why Donald Trump Eats 100 Cheeseburgers a Day." This is an article that even I, a journalist, don't know how I'd go about writing.

The business of communicating the happenings of society might seem to be something entirely human-centric. After all, what do machines care about what happens in the news? However, the journalism industry as a whole is highly dependent on technology. From ways of communicating instantly with news sources and content generated first-hand in image, video, and text form, to the publication of content across an ever-increasing array of channels, technology is core to making the news and journalism industry buzz at the pace that news is generated. However, it's not just that news and journalism is a consumer of technology, but increasingly technology itself is changing the way that news and journalism is done. In particular, news organizations are increasingly leveraging AI to change the way news is generated, produced, published, and shared.

But today all thanks to Artificial Intelligence, beauty regime has taken a more high-tech twist.