The telecoms operator's much-awaited new set-top box, dubbed Freebox Delta and designed by Jasper Morrison, will also contained an improved internet speed and data storage, as well as the audio technology of French start-up firm Devialet.

An article by Apple describing how the use machine learning to improve HomePod's signal processing system to remove echo, background noise and separate sound sources.

Already, AI is more widespread than many of us may realise. Spotify, Netflix and Amazon all use it to assist in the way they target their products to consumers--while Amazon has also used it to reduce labour costs, effectively replacing six humans with one robot. In this article, we take a look at how investors should think about AI, how widespread it is, and why this thematic is likely to have a far-reaching impact on economies and markets. We also examine why those companies that are embracing AI are likely to be able to both disrupt and deliver real value to their shareholders. At a company level, the ability to implement and leverage AI will become one of the key differentiators over the next decade.

The dawn of 8K resolution television has arrived as a Japanese television network broadcasts the first programmes in the format. Japan's national public broadcasting organisation, NHK, is now sending both 4K and 8K channels via satellite to viewers. The first film shown in 8K will be '2001: A Space Odyssey' and Stanley Kubrick's classic film was scanned by Warned Bros on 70mm film negatives to produce the high-resolution masterpiece. This newest resolution has four times as many pixels vertically and horizontally as current top-end 4K Ultra HD screens, and 16 times more than standard 1080p HD. NHK is currently the only channel pushing for the 8K medium as specialist equipment and huge price-tags puts off consumers and companies alike.

In this paper, we propose a movie genre recommendation system based on imbalanced survey data and unequal classification costs for small and medium-sized enterprises (SMEs) who need a data-based and analytical approach to stock favored movies and target marketing to young people. The dataset maintains a detailed personal profile as predictors including demographic, behavioral and preferences information for each user as well as imbalanced genre preferences. These predictors do not include movies' information such as actors or directors. The paper applies Gentle boost, Adaboost and Bagged tree ensembles as well as SVM machine learning algorithms to learn classification from one thousand observations and predict movie genre preferences with adjusted classification costs. The proposed recommendation system also selects important predictors to avoid overfitting and to shorten training time. This paper compares the test error among the above-mentioned algorithms that are used to recommend different movie genres. The prediction power is also indicated in a comparison of precision and recall with other state-of-the-art recommendation systems. The proposed movie genre recommendation system solves problems such as small dataset, imbalanced response, and unequal classification costs.

Separating a singing voice from its music accompaniment remains an important challenge in the field of music information retrieval. We present a unique neural network approach inspired by a technique that has revolutionized the field of vision: pixel-wise image classification, which we combine with cross entropy loss and pretraining of the CNN as an autoencoder on singing voice spectrograms. The pixel-wise classification technique directly estimates the sound source label for each time-frequency (T-F) bin in our spectrogram image, thus eliminating common pre-and postprocessing tasks. The proposed network is trained by using the Ideal Binary Mask (IBM) as the target output label. The IBM identifies the dominant sound source in each T-F bin of the magnitude spectrogram of a mixture signal, by considering each T-F bin as a pixel with a multi-label (for each sound source). Cross entropy is used as the training objective, so as to minimize the average probability error between the target and predicted label for each pixel. By treating the singing voice separation problem as a pixel-wise classification task, we additionally eliminate one of the commonly used, yet not easy to comprehend, postprocessing steps: the Wiener filter postprocessing. The proposed CNN outperforms the first runner up in the Music Information Retrieval Evaluation eXchange (MIREX) 2016 and the winner of MIREX 2014 with a gain of 2.2702 5.9563 dB global normalized source to distortion ratio (GNSDR) when applied to the iKala dataset. This work is supported by the MOE Academic fund AFD 05/15 SL and SUTD SRG ISTD 2017 129. Corresponding Author D. Herremans Singapore University of Technology and Design, Singapore & Institute for High Performance Computing, A*STAR, Singapore Email: dorien herremans@sutd.edu.sg 1 INTRODUCTION to compete with cutting-edge singing voice separation systems which use multichannel modeling,data augmentation, and model blending. Keywords Singing Voice Separation · Convolutional Neural Network · Ideal Binary Mask · Cross Entropy · Pixel-wise Image Classification 1 Introduction Humans have an exceptional ability to separate different sounds from a musical signal [3]. For instance, some musicians can distinguish the guitar part from a song and transcribe it; and most non-musician listeners are able to hear and sing along to lyrics of a song.

This paper describes the Tartan conversational agent built for the 2018 Alexa Prize Competition. Tartan is a non-goal-oriented socialbot focused around providing users with an engaging and fluent casual conversation. Tartan's key features include an emphasis on structured conversation based on flexible finite-state models and an approach focused on understanding and using conversational acts. To provide engaging conversations, Tartan blends script-like yet dynamic responses with data-based generative and retrieval models. Unique to Tartan is that our dialog manager is modeled as a dynamic Finite State Machine. To our knowledge, no other conversational agent implementation has followed this specific structure.

Open your Facebook feed, a newspaper or turn on the news and you'll likely see something about the dangers of machine learning, the increasing amount of fake news or even the dangers of AI on our privacy. Yet, these technologies are continuing to develop and thanks to new developments in automation and machine deception – they will continue to shape the use of AI over the coming year. While full automation might still be a way off, there are many workflows and tasks that lend themselves to partial automation. In fact, McKinsey estimates that "fewer than 5 per cent of occupations can be entirely automated using current technology. However, about 60 per cent of occupations could have 30 per cent or more of their constituent activities automated."

A model of music needs to have the ability to recall past details and have a clear, coherent understanding of musical structure. Detailed in the paper is a deep reinforcement learning architecture that predicts and generates polyphonic music aligned with musical rules. The probabilistic model presented is a Bi-axial LSTM trained with a pseudo-kernel reminiscent of a convolutional kernel. To encourage exploration and impose greater global coherence on the generated music, a deep reinforcement learning approach DQN is adopted. When analyzed quantitatively and qualitatively, this approach performs well in composing polyphonic music.

I'm looking for an implementation of a Regression Tree from scratch and have only been able to find classification trees.