… by limiting how long your iPhone remained fully charged by pausing the charging process at 80 percent, and then using on-device machine learning …

The new feature is based on Google's Looking to Listen machine learning (ML) technology, which uses both visual and audio cues for isolating and …

Known as "King Abdulaziz University and University of Oxford's Center for Artificial Intelligence in Precision Medicines," it will be established at the two …

A few weeks ago I came across with an interesting article titled "A Music Taste Analysis Using Spotify API and Python". In this article, an author tries to analyze not only his but also a fiancée's preference to determine what the data has to say about this. Thus, he compares two different profiles in terms of music features, which are provided by Spotify's API, simultaneously. While reading it, I was curious to not only analyze my own preference but also to play with my Spotify data. Therefore, I wanted to cluster my saved songs on Spotify into separate playlists that would represent a specific mood I have while listening to them. Initially, it is worth mentioning that likewise Twitter, Slack, and Facebook Spotify offers an API for developers to explore their music database and get insights into our listening habits.

Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous studies do not explicitly model factual description of products, tending to generate uninformative content. Moreover, they mainly focus on word-level generation, but cannot accurately reflect more abstractive user preference in multiple aspects. To address the above issues, we propose a novel knowledge-enhanced PRG model based on capsule graph neural network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG) for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules for encoding underlying characteristics from the HKG. Our generation process contains two major steps, namely aspect sequence generation and sentence generation. First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence. Then, conditioned on the inferred aspect label, we design a graph-based copy mechanism to generate sentences by incorporating related entities or words from HKG. To our knowledge, we are the first to utilize knowledge graph for the PRG task. The incorporated KG information is able to enhance user preference at both aspect and word levels. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our model on the PRG task.

Ranking items by their probability of relevance has long been the goal of conventional ranking systems. While this maximizes traditional criteria of ranking performance, there is a growing understanding that it is an oversimplification in online platforms that serve not only a diverse user population, but also the producers of the items. In particular, ranking algorithms are expected to be fair in how they serve all groups of users -- not just the majority group -- and they also need to be fair in how they divide exposure among the items. These fairness considerations can partially be met by adding diversity to the rankings, as done in several recent works, but we show in this paper that user fairness, item fairness and diversity are fundamentally different concepts. In particular, we find that algorithms that consider only one of the three desiderata can fail to satisfy and even harm the other two. To overcome this shortcoming, we present the first ranking algorithm that explicitly enforces all three desiderata. The algorithm optimizes user and item fairness as a convex optimization problem which can be solved optimally. From its solution, a ranking policy can be derived via a new Birkhoff-von Neumann decomposition algorithm that optimizes diversity. Beyond the theoretical analysis, we provide a comprehensive empirical evaluation on a new benchmark dataset to show the effectiveness of the proposed ranking algorithm on controlling the three desiderata and the interplay between them.

We present Mask-guided Generative Adversarial Network (MagGAN) for high-resolution face attribute editing, in which semantic facial masks from a pre-trained face parser are used to guide the fine-grained image editing process. With the introduction of a mask-guided reconstruction loss, MagGAN learns to only edit the facial parts that are relevant to the desired attribute changes, while preserving the attribute-irrelevant regions (e.g., hat, scarf for modification `To Bald'). Further, a novel mask-guided conditioning strategy is introduced to incorporate the influence region of each attribute change into the generator. In addition, a multi-level patch-wise discriminator structure is proposed to scale our model for high-resolution ($1024 \times 1024$) face editing. Experiments on the CelebA benchmark show that the proposed method significantly outperforms prior state-of-the-art approaches in terms of both image quality and editing performance.

With disinformation on social media a significant problem, the ability to identify authors of malicious articles and the originators of disinformation campaigns could help reduce the threat from such information attacks. At the Black Hat Asia 2020 conference this week, three researchers from Baidu Security, the cybersecurity division of the Chinese technology giant Baidu, presented their approach to identifying authors based on machine learning techniques, such as neural networks. The researchers used 130,000 articles by more than 3,600 authors scraped from eight websites to train a neural network that could identify an author from a group of five possible writers 93% of the time and identify an author from a group of 2,000 possible writers 27% of the time. While the results are not impressive, they do show that identifying the person behind a piece of writing is possible, said Li Yiping, a researcher at Baidu Security, during his presentation on his team's work. "Most fake news is posted anonymously and lacks valid information to identify the author," he said.

"Artificial Intelligence and Media," the new 18-page special report from Variety Intelligence Platform (VIP), explores how the transformation of the movie, music and other media and entertainment industries over the past two decades, from ones based primarily on production and distribution of physical media via analog channels to ones based on digital formats and platforms, has yielded a torrent of consumer usage, preference and behavior that can be harnessed and applied across a plethora of entertainment-related functions.

On the PlayStation, the default controls assign throttle controls and rolling to the left thumb stick, while the right handles pitch and yaw. It's an unwieldy combo that led me to performing, let's say, unorthodox maneuvers instead of simply making the ship go where I wanted. I also had a tendency to spin like crazy. While young Anakin would have been proud of me, it was a little frustrating at first. One mission, which requires you to rotate your Y-wing to drop downward-firing bombs while skimming the trench-like surface of a space station, resulted in more than a few crashes.