Two chefs get blasted in the face with a mystery entree on Dishmantled. Two chefs get blasted in the face with a mystery entree on Dishmantled. The streaming service Quibi -- short for "quick bites" -- calls itself "the first entertainment platform designed specifically for your phone." Perfect for the busy, distracted, on-the-go consumer! Too bad none of us are on-the-going anywhere, these days.

The information ecosystem today is overwhelmed by an unprecedented quantity of data on versatile topics are with varied quality. However, the quality of information disseminated in the field of medicine has been questioned as the negative health consequences of health misinformation can be life-threatening. There is currently no generic automated tool for evaluating the quality of online health information spanned over a broad range. To address this gap, in this paper, we applied a data mining approach to automatically assess the quality of online health articles based on 10 quality criteria. We have prepared a labeled dataset with 53012 features and applied different feature selection methods to identify the best feature subset with which our trained classifier achieved an accuracy of 84%-90% varied over 10 criteria. Our semantic analysis of features shows the underpinning associations between the selected features & assessment criteria and further rationalize our assessment approach. Our findings will help in identifying high-quality health articles and thus aiding users in shaping their opinion to make the right choice while picking health-related help from online.

The current tsunami of deep learning (the hyper-vitamined return of artificial neural networks) applies not only to traditional statistical machine learning tasks: prediction and classification (e.g., for weather prediction and pattern recognition), but has already conquered other areas, such as translation. A growing area of application is the generation of creative content: in particular the case of music, the topic of this paper. The motivation is in using the capacity of modern deep learning techniques to automatically learn musical styles from arbitrary musical corpora and then to generate musical samples from the estimated distribution, with some degree of control over the generation. This article provides a survey of music generation based on deep learning techniques. After a short introduction to the topic illustrated by a recent exemple, the article analyses some early works from the late 1980s using artificial neural networks for music generation and how their pioneering contributions foreshadowed current techniques. Then, we introduce some conceptual framework to analyze the various concepts and dimensions involved. Various examples of recent systems are introduced and analyzed to illustrate the variety of concerns and of techniques.

Britt argues that artificial intelligence – when used ethically – is positively impacting people when it comes to hiring and the employee experience. He expands further by saying that AI should be used to enhance human capability and help workers improve and grow – not to place a "big brother" monitoring system on them. Digital Journal spoke with CallMiner's Britt about examples of how AI has helped companies develop and retain top talent, simplify jobs, and drastically improve the employee experience. Digital Journal: How is artificial intelligence disrupting business? Britt: If by disruption we mean a radical change in a business, then the most disruptive aspects of AI is the need to rethink existing human-built processes to be AI processes, basically transforming a business from Human-to-Human to Machine-to-Human.

This research work deals with Natural Language Processing (NLP) and extraction of essential information in an explicit form. The most common among the information management strategies is Document Retrieval (DR) and Information Filtering. DR systems may work as combine harvesters, which bring back useful material from the vast fields of raw material. With large amount of potentially useful information in hand, an Information Extraction (IE) system can then transform the raw material by refining and reducing it to a germ of original text. A Document Retrieval system collects the relevant documents carrying the required information, from the repository of texts. An IE system then transforms them into information that is more readily digested and analyzed. It isolates relevant text fragments, extracts relevant information from the fragments, and then arranges together the targeted information in a coherent framework. The thesis presents a new approach for Word Sense Disambiguation using thesaurus. The illustrative examples supports the effectiveness of this approach for speedy and effective disambiguation. A Document Retrieval method, based on Fuzzy Logic has been described and its application is illustrated. A question-answering system describes the operation of information extraction from the retrieved text documents. The process of information extraction for answering a query is considerably simplified by using a Structured Description Language (SDL) which is based on cardinals of queries in the form of who, what, when, where and why. The thesis concludes with the presentation of a novel strategy based on Dempster-Shafer theory of evidential reasoning, for document retrieval and information extraction. This strategy permits relaxation of many limitations, which are inherent in Bayesian probabilistic approach.

We present an analysis of semi-supervised acoustic and language model training for English-isiZulu code-switched ASR using soap opera speech. Approximately 11 hours of untranscribed multilingual speech was transcribed automatically using four bilingual code-switching transcription systems operating in English-isiZulu, English-isiXhosa, English-Setswana and English-Sesotho. These transcriptions were incorporated into the acoustic and language model training sets. Results showed that the TDNN-F acoustic models benefit from the additional semi-supervised data and that even better performance could be achieved by including additional CNN layers. Using these CNN-TDNN-F acoustic models, a first iteration of semi-supervised training achieved an absolute mixed-language WER reduction of 3.4%, and a further 2.2% after a second iteration. Although the languages in the untranscribed data were unknown, the best results were obtained when all automatically transcribed data was used for training and not just the utterances classified as English-isiZulu. Despite reducing perplexity, the semi-supervised language model was not able to improve the ASR performance.

Generating music with emotion similar to that of an input video is a very relevant issue nowadays. Video content creators and automatic movie directors benefit from maintaining their viewers engaged, which can be facilitated by producing novel material eliciting stronger emotions in them. Moreover, there's currently a demand for more empathetic computers to aid humans in applications such as augmenting the perception ability of visually and/or hearing impaired people. Current approaches overlook the video's emotional characteristics in the music generation step, only consider static images instead of videos, are unable to generate novel music, and require a high level of human effort and skills. In this study, we propose a novel hybrid deep neural network that uses an Adaptive Neuro-Fuzzy Inference System to predict a video's emotion from its visual features and a deep Long Short-Term Memory Recurrent Neural Network to generate its corresponding audio signals with similar emotional inkling. The former is able to appropriately model emotions due to its fuzzy properties, and the latter is able to model data with dynamic time properties well due to the availability of the previous hidden state information. The novelty of our proposed method lies in the extraction of visual emotional features in order to transform them into audio signals with corresponding emotional aspects for users. Quantitative experiments show low mean absolute errors of 0.217 and 0.255 in the Lindsey and DEAP datasets respectively, and similar global features in the spectrograms. This indicates that our model is able to appropriately perform domain transformation between visual and audio features. Based on experimental results, our model can effectively generate audio that matches the scene eliciting a similar emotion from the viewer in both datasets, and music generated by our model is also chosen more often.

Machine learning techniques, such as reinforcement learning models, are now playing a crucial role in the development of smart and efficient robots.

Hi everyone, I've recently built Mimicry, a PyTorch library for GANs which I hope can make GAN research findings more reproducible. The general idea is to have an easily accessible set of implementations (that reproduce the original scores as closely as possible), baseline scores for comparisons, and metrics for GANs which researchers can quickly use to produce results and compare. For reproducibility, I re-implemented the original models and verified their correctness by checking their scores against the reported ones under the same training and evaluation conditions. On the metrics part, to ensure backward compatibility of existing scores, I adopted the original TensorFlow implementations of Inception Score, FID, and KID so new scores produced can be compared with other works directly. I've also included a tutorial to implement a more sophisticated GAN like Self-supervised GAN (SSGAN) from the ground up, again with a focus on reproducing the results.

Artificial Intelligence (AI) is having a transformative effect on a huge range of industries, and the world of media and entertainment is no exception. Creators and machines are continuing to become more intertwined, with creative workflows taking on new shapes as AI-assistance gathers momentum. At a broad level, people are recognising that technology and creativity go hand in hand. Creative professionals are expressing an interest in how AI and machine learning can aid the creative process. And although the discussion about machines replacing humans remains prevalent, the reality is much less dystopian.