Imagine trying to learn Chinese by reading hundreds of books containing only Chinese characters, and absolutely nothing else. Or trying to teach a new language to a 1 year old baby by giving him the text of hundreds of English books written in the past few years. Given hundreds of books written in Chinese, a rare megasavant (with a nearly eidetic memory) will possibly learn to identify certain patterns in the text, and use those patterns to generate coherent Chinese text. This is exactly what neural networks do, they learn to generate meaningful sequence of characters/words. However, neither the savant nor the neural network can be expected to know the meaning of the Chinese text that they generate, they are simply copying partial sequences from their memory.

The brightest stars in the music world are shining at the Grammy Awards, as Music's Biggest Night rolls on. To get the inside scoop on this year's ceremony, Slate talked to the ultimate insider: the Recording Academy Bot that appears in a popup window whenever you visit Grammy.com. Most people wait all year for the Grammys, but for the Recording Academy Bot--programmed with no concept of linear time--it's always the Grammys. It knows no other reality! We asked the bot about red carpet fashion, what to look for at this year's awards, and--sorry, Recording Academy!--why the ceremony always takes so long.

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.

Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. Moreover, news recommendation also faces the challenges of high time-sensitivity of news and dynamic diversity of users' interests. To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users' diverse interests, we also design an attention module in DKN to dynamically aggregate a user's history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.

We introduce contextual explanation networks (CENs)---a class of models that learn to predict by generating and leveraging intermediate explanations. CENs are deep networks that generate parameters for context-specific probabilistic graphical models which are further used for prediction and play the role of explanations. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain jointly. Our approach offers two major advantages: (i) for each prediction, valid instance-specific explanations are generated with no computational overhead and (ii) prediction via explanation acts as a regularization and boosts performance in low-resource settings. We prove that local approximations to the decision boundary of our networks are consistent with the generated explanations. Our results on image and text classification and survival analysis tasks demonstrate that CENs are competitive with the state-of-the-art while offering additional insights behind each prediction, valuable for decision support.

Recently, I've been trying to figure out new and interesting ways to combine deep learning and finance (totally not for my master thesis or anything like that). I've read that CNN and their variations could be applied to predict financial things like stock prices to somewhat decent extent. What do you think about applying regional CNN to predict-stock prices. The idea is quite simple: instead of looking at the whole graph CNN would look at regions who express high heteroscedasticity or have a clear upwards or downwards trend. Based on the amount of differences expressed and how far these regions would be from our prediction point (I guess one could use something like euclidean distance).

HBO's version of Westworld seems tailor-made for a video game: it's a fully-realized robot theme park with plenty of opportunities for disaster. And sure enough, you're about to get one. In the wake of a teaser on the website of the show's fictitious Delos Incorporated, Warner Bros. Interactive Entertainment has confirmed to Engadget that a mobile game is in development. The title is "currently being tested in limited release," a spokesperson said. The teaser itself may offer enough clues as it is.

I have an upcoming interview that involves applying Deep Learning to Computer Vision problems. Though I have experience with deep learning I'm currently weak on the pure Computer Vision side of things. What are the topics that I should revise? What questions might be asked?

How Elton John's VR retirement announcement hit your headset Emma Grey Ellis, Wired If you haven't heard, music legend Sir Elton John announced his retirement and final tour this week in the most 2018 way possible: VR. Wired goes behind the scenes to get the details on how that montage was made before it beamed out to your headset. MLB's Advanced Media arm does a lot of things -- from websites to streaming. It's also working on a video game and Polygon has a detailed look at the development. Spotify's scientist: Artificial intelligence should be embraced, not feared, by the music business Tim Ingham, Music Business Worldwide MBW caught up with the director of Spotify's Creator Technology Research Lab to chat about AI and how it relates to the future of the biz, including robot musicians.

A desktop-based machine learning application is being used to create fake pornographic videos featuring celebrities.