"We are thrilled that the discerning panel of judges at InnoSTARS agreed that our artificial intelligence music technology represents a paradigm shift in …

The artificial intelligence ETF will create a portfolio of between 80 and 250 stocks -- choosing from more than 15,000 companies across the globe.

Sonos has revealed the Beam, a speaker that sits under your TV. The company's pitch is that the device is really three different things all packed into one box: not only a soundbar, but also a music player and a smart assistant, as well. And in fact it is more than one smart assistant, with Sonos announcing at the same event that Siri will now be able to talk to Alexa, and that Google Assistant will also eventually be coming to its speakers. But another important point is that box all of that is crammed into is much smaller than its other devices. Until now, its only TV speakers have been the Playbase and the Playbar, which either sit beneath or in front of the television and sweep all the way in front of it.

This document contains the outcome of the first Human behaviour and machine intelligence (HUMAINT) workshop that took place 5-6 March 2018 in Barcelona, Spain. The workshop was organized in the context of a new research programme at the Centre for Advanced Studies, Joint Research Centre of the European Commission, which focuses on studying the potential impact of artificial intelligence on human behaviour. The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs. The document is made of short position statements and identification of challenges provided by each expert, and incorporates the result of the discussions carried out during the workshop. In the conclusion section, we provide a list of emerging research topics and strategies to be addressed in the near future.

We present color sails, a discrete-continuous color gamut representation that extends the color gradient analogy to three dimensions and allows interactive control of the color blending behavior. Our representation models a wide variety of color distributions in a compact manner, and lends itself to applications such as color exploration for graphic design, illustration and similar fields. We propose a Neural Network that can fit a color sail to any image. Then, the user can adjust color sail parameters to change the base colors, their blending behavior and the number of colors, exploring a wide range of options for the original design. In addition, we propose a Deep Learning model that learns to automatically segment an image into color-compatible alpha masks, each equipped with its own color sail. This allows targeted color exploration by either editing their corresponding color sails or using standard software packages. Our model is trained on a custom diverse dataset of art and design. We provide both quantitative evaluations, and a user study, demonstrating the effectiveness of color sail interaction. Interactive demos are available at www.colorsails.com.

We revisit the importance of the individual units in Convolutional Neural Networks (CNNs) for visual recognition. By conducting unit ablation experiments on CNNs trained on large scale image datasets, we demonstrate that, though ablating any individual unit does not hurt overall classification accuracy, it does lead to significant damage on the accuracy of specific classes. This result shows that an individual unit is specialized to encode information relevant to a subset of classes. We compute the correlation between the accuracy drop under unit ablation and various attributes of an individual unit such as class selectivity and weight L1 norm. We confirm that unit attributes such as class selectivity are a poor predictor for impact on overall accuracy as found previously in recent work \cite{morcos2018importance}. However, our results show that class selectivity along with other attributes are good predictors of the importance of one unit to individual classes. We evaluate the impact of random rotation, batch normalization, and dropout to the importance of units to specific classes. Our results show that units with high selectivity play an important role in network classification power at the individual class level. Understanding and interpreting the behavior of these units is necessary and meaningful.

Sentiment classification involves quantifying the affective reaction of a human to a document, media item or an event. Although researchers have investigated several methods to reliably infer sentiment from lexical, speech and body language cues, training a model with a small set of labeled datasets is still a challenge. For instance, in expanding sentiment analysis to new languages and cultures, it may not always be possible to obtain comprehensive labeled datasets. In this paper, we investigate the application of semi-supervised and transfer learning methods to improve performances on low resource sentiment classification tasks. We experiment with extracting dense feature representations, pre-training and manifold regularization in enhancing the performance of sentiment classification systems. Our goal is a coherent implementation of these methods and we evaluate the gains achieved by these methods in matched setting involving training and testing on a single corpus setting as well as two cross corpora settings. In both the cases, our experiments demonstrate that the proposed methods can significantly enhance the model performance against a purely supervised approach, particularly in cases involving a handful of training data.

As smart speakers gained popularity, a big name in the space seemed to have fallen by the wayside. Sonos pioneered a high-quality connected home-theater speaker system in the early 2000s. In 2016, it began making a shift to support streaming music services on its products. It shipped its first assistant-laden speaker, the Alexa-enabled Sonos One, in 2017, but has now taken that idea even further. The company, on Wednesday, unveiled Beam, a smart sound bar, which will soon be virtual assistant agnostic: It's shipping with Alexa, gaining Siri control with AirPlay 2 in July, and adding Google Assistant compatibility later this year.

Sonos took the wraps off its latest smart speaker at an event in San Francisco earlier today. The $399 Sonos Beam is smaller and less expensive than either the Sonos Playbase or the older Sonos Playbar, and it will support Apple's AirPlay 2 multi-room audio technology when it ships in July. "All the tech giants are building smart speakers," said Chris Kallai, Sonos' VP of Hardware Product Management, when introducing the Sonos Beam. "But they're not doing it so you can listen to all the music you want. Kallai said the Beam combines three products into one: "Alexa, Apple Music, and a compact soundbar."

Close your eyes and try to picture a journalist. Well, what if all of that was replaced … by robots? Okay, our show isn't about to be hosted by a machine (yet). But artificial intelligence is already being used in newsrooms today. For instance, there's Heliograf, a bot developed by The Washington Post.