A'widely-used' algorithm on streaming services including Spotify is more likely to recommend songs by male musicians than female musicians, a new study finds. European researchers who analysed the listening habits of 330,000 people over nine years found only 25 per cent of the artists ever listened to were female. When they tested the algorithm they found, on average, the first recommended track was by a man, along with the next six, and users had to wait until song seven or eight to hear a song by a woman. Stats have already suggested female artists don't get as much exposure as male artists – the 2020 Spotify Wrapped statistics showed that the top five most streamed artists were all male, with similar trends across all categories. 'We showed a widely used recommendation algorithm is more likely to pick music by male than female artists,' said study author Dr Christine Bauer at Utrecht University in a piece for the Conversation.

We introduce a non-parametric approach for infinite video texture synthesis using a representation learned via contrastive learning. We take inspiration from Video Textures, which showed that plausible new videos could be generated from a single one by stitching its frames together in a novel yet consistent order. This classic work, however, was constrained by its use of hand-designed distance metrics, limiting its use to simple, repetitive videos. We draw on recent techniques from self-supervised learning to learn this distance metric, allowing us to compare frames in a manner that scales to more challenging dynamics, and to condition on other data, such as audio. We learn representations for video frames and frame-to-frame transition probabilities by fitting a video-specific model trained using contrastive learning. To synthesize a texture, we randomly sample frames with high transition probabilities to generate diverse temporally smooth videos with novel sequences and transitions. The model naturally extends to an audio-conditioned setting without requiring any finetuning. Our model outperforms baselines on human perceptual scores, can handle a diverse range of input videos, and can combine semantic and audio-visual cues in order to synthesize videos that synchronize well with an audio signal.

The advent of deep learning has brought a significant improvement in the quality of generated media. However, with the increased level of photorealism, synthetic media are becoming hardly distinguishable from real ones, raising serious concerns about the spread of fake or manipulated information over the Internet. In this context, it is important to develop automated tools to reliably and timely detect synthetic media. In this work, we analyze the state-of-the-art methods for the detection of synthetic images, highlighting the key ingredients of the most successful approaches, and comparing their performance over existing generative architectures. We will devote special attention to realistic and challenging scenarios, like media uploaded on social networks or generated by new and unseen architectures, analyzing the impact of suitable augmentation and training strategies on the detectors' generalization ability.

Video compression is a critical component of Internet video delivery. Recent work has shown that deep learning techniques can rival or outperform human-designed algorithms, but these methods are significantly less compute and power-efficient than existing codecs. This paper presents a new approach that augments existing codecs with a small, content-adaptive super-resolution model that significantly boosts video quality. Our method, SRVC, encodes video into two bitstreams: (i) a content stream, produced by compressing downsampled low-resolution video with the existing codec, (ii) a model stream, which encodes periodic updates to a lightweight super-resolution neural network customized for short segments of the video. SRVC decodes the video by passing the decompressed low-resolution video frames through the (time-varying) super-resolution model to reconstruct high-resolution video frames. Our results show that to achieve the same PSNR, SRVC requires 16% of the bits-per-pixel of H.265 in slow mode, and 2% of the bits-per-pixel of DVC, a recent deep learning-based video compression scheme. SRVC runs at 90 frames per second on a NVIDIA V100 GPU.

AI is not anymore a tool of the media industry where it just serves to solve simple use cases with simple AI algorithms.

… yielded a lot of thoughts and insights, so many in fact that Datar–a self-described geek–says he is using machine learning to absorb them.

GeoMark's RFDbase contains raw data from every major petroleum basin in the world that …

"The issue is that deep learning-based neural language generation models … "The current research in machine learning and AI primarily focus on …

The April 6 webinar, Clinical Development Innovations, will touch upon artificial intelligence, machine learning, ethics and other impactful issues.

But the real promise will come through the learning aspect of artificial intelligence, what's known as machine learning.