Look anywhere in the news these days and you'll read about the latest updates in artificial intelligence and smart technology. Although these emerging technologies is scary for some people, as communications professionals it's important to consider the future of communicating and how we must adapt in accordance to change. VR headsets, open-source gaming, QR advertising and messenger marketing are just a few examples of areas that are now fair game for reaching audiences as we work in the public relations and communications industry. We need to seriously consider all areas within which people are adapting their personal communication in order to do our clients justice when broadcasting their messages and listening to their audiences. AI is already at play in a lot of areas across the PR and communications industry: scheduling messages, automating responses and creating content from a list of pre-defined sources.

Look anywhere in the news these days and you'll read about the latest updates in artificial intelligence and smart technology. Although these emerging technologies is scary for some people, as communications professionals it's important to consider the future of communicating and how we must adapt in accordance to change. VR headsets, open-source gaming, QR advertising and messenger marketing are just a few examples of areas that are now fair game for reaching audiences as we work in the public relations and communications industry. We need to seriously consider all areas within which people are adapting their personal communication in order to do our clients justice when broadcasting their messages and listening to their audiences. AI is already at play in a lot of areas across the PR and communications industry: scheduling messages, automating responses and creating content from a list of pre-defined sources.

New York:Thomas Tull scored a blockbuster ending to his Hollywood career. He sold his production company, Legendary Entertainment, to Dalian Wanda Group for $3.8 billion in 2016 after using data analytics for the production, marketing and distribution of blockbusters including "Inception," "The Dark Knight Rises," and "Jurassic World." Tull, 48, is adopting a similar approach with his latest venture. Tulco, based in his adopted hometown of Pittsburgh, is an investment holding company that uses artificial intelligence, machine learning and data analytics to identify investments and grow the businesses. In a phone interview with Bloomberg, Tull discussed this approach, negotiating tactics and winning a Super Bowl with the Pittsburgh Steelers. Comments have been edited and condensed.

Quantized Neural Networks (QNNs) are often used to improve network efficiency during the inference phase, i.e. after the network has been trained. Extensive research in the field suggests many different quantization schemes. Still, the number of bits required, as well as the best quantization scheme, are yet unknown. Our theoretical analysis suggests that most of the training process is robust to substantial precision reduction, and points to only a few specific operations that require higher precision. Armed with this knowledge, we quantize the model parameters, activations and layer gradients to 8-bit, leaving at a higher precision only the final step in the computation of the weight gradients. Additionally, as QNNs require batch-normalization to be trained at high precision, we introduce Range Batch-Normalization (BN) which has significantly higher tolerance to quantization noise and improved computational complexity. Our simulations show that Range BN is equivalent to the traditional batch norm if a precise scale adjustment, which can be approximated analytically, is applied. To the best of the authors' knowledge, this work is the first to quantize the weights, activations, as well as a substantial volume of the gradients stream, in all layers (including batch normalization) to 8-bit while showing state-of-the-art results over the ImageNet-1K dataset.

In this paper, we propose multi-variable LSTM capable of accurate forecasting and variable importance interpretation for time series with exogenous variables. Current attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To this end, the multi-variable LSTM equipped with tensorized hidden states is developed to learn hidden states for individual variables, which give rise to our mixture temporal and variable attention. Based on such attention mechanism, we infer and quantify variable importance. Extensive experiments using real datasets with Granger-causality test and the synthetic dataset with ground truth demonstrate the prediction performance and interpretability of multi-variable LSTM in comparison to a variety of baselines. It exhibits the prospect of multi-variable LSTM as an end-to-end framework for both forecasting and knowledge discovery.

We propose a novel method for compressed sensing recovery using untrained deep generative models. Our method is based on the recently proposed Deep Image Prior (DIP), wherein the convolutional weights of the network are optimized to match the observed measurements. We show that this approach can be applied to solve any differentiable inverse problem. We also introduce a novel learned regularization technique which incorporates a small amount of prior information, further reducing the number of measurements required for a given reconstruction error. Our algorithm requires approximately 4 - 6 fewer measurements than classical Lasso methods. Unlike previous approaches based on generative models, our method does not require the model to be pre-trained. As such, we can apply our method to various medical imaging datasets for which data acquisition is expensive and no known generative models exist.

We consider the problem of learning to play first-person shooter (FPS) video games using raw screen images as observations and keyboard inputs as actions. The high-dimensionality of the observations in this type of applications leads to prohibitive needs of training data for model-free methods, such as the deep Q-network (DQN), and its recurrent variant DRQN. Thus, recent works focused on learning low-dimensional representations that may reduce the need for data. This paper presents a new and efficient method for learning such representations. Salient segments of consecutive frames are detected from their optical flow, and clustered based on their feature descriptors. The clusters typically correspond to different discovered categories of objects. Segments detected in new frames are then classified based on their nearest clusters. Because only a few categories are relevant to a given task, the importance of a category is defined as the correlation between its occurrence and the agent's performance. The result is encoded as a vector indicating objects that are in the frame and their locations, and used as a side input to DRQN. Experiments on the game Doom provide a good evidence for the benefit of this approach.

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.

Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their sememes by linguists, and form linguistic common-sense knowledge bases widely used in various NLP tasks. Recently, the lexical sememe prediction task has been introduced. It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency. However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-of-vocabulary words. To address this issue for Chinese, we propose a novel framework to take advantage of both internal character information and external context information of words. We experiment on HowNet, a Chinese sememe knowledge base, and demonstrate that our framework outperforms state-of-the-art baselines by a large margin, and maintains a robust performance even for low-frequency words.

Conversational systems have become increasingly popular as a way for humans to interact with computers. To be able to provide intelligent responses, conversational systems must correctly model the structure and semantics of a conversation. We introduce the task of measuring semantic (in)coherence in a conversation with respect to background knowledge, which relies on the identification of semantic relations between concepts introduced during a conversation. We propose and evaluate graph-based and machine learning-based approaches for measuring semantic coherence using knowledge graphs, their vector space embeddings and word embedding models, as sources of background knowledge. We demonstrate how these approaches are able to uncover different coherence patterns in conversations on the Ubuntu Dialogue Corpus.