Music recommendation algorithms could be amplifying the industry's existing gender bias problem, according to a study that proposes a new method allowing greater exposure for female artists. The existance of a gender bias in the music industry is not unknown. For instance, a study of the top five music charts in the UK between the years 1960-1995 showed how popular music is affected by a large gender inequality with a bias in listening preferences towards male artists. A recent analysis of music festivals found that lineups are heavily skewed towards male performers and this bias is also said to be prevalent in music streaming apps like Spotify. A growing number of people use streaming platforms to listen to music, and these apps use algorithms to recommend songs based on the users' listening habits.

Are machines biased? The FTC has issued Business Guidance about the use of artificial intelligence (AI), warning marketers about the danger of the …

In statistical learning theory, there is something called the VC Dimension of an algorithm (https://en.m.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_dimension) - the VC dimension is apparently what describes the relartive level of complexity a machine learning algorithm can capture. Does this concept of VC Dimension carry over to models in time series analysis? Is it possible to show that LSTM's have a higher VC dimension compared to ARIMA style models? Supposedly, neural network based time series models were developed because modeols like ARIMA was unable to provide reliable estimates for bigger and complex datasets. Mathematically speaking, what allows a LSTM to capture more variation and complexity in a dataset compared to ARIMA?

You might not know this, but the metaverse is coming. Where you can live a life that expands on reality and can approach hyperrealism. I just took my next big step toward embracing it – and soon you may, too. Within a matter of minutes, I was reborn as a holographic avatar – a digital version of me – with the help of the Avatar Dimension technicians in northern Virginia, just west of the nation's capital. My virtual doppelgänger is ready to embark on digital adventures, be inserted into a video game, a movie or virtual reality. And it's ready for the metaverse, the persistent alternate reality in cyberspace author Neal Stephenson envisioned in his 1992 science fiction novel "Snow Crash."

Reinforcement learning techniques achieved human-level performance in several tasks in the last decade. However, in recent years, the need for interpretability emerged: we want to be able to understand how a system works and the reasons behind its decisions. Not only we need interpretability to assess the safety of the produced systems, we also need it to extract knowledge about unknown problems. While some techniques that optimize decision trees for reinforcement learning do exist, they usually employ greedy algorithms or they do not exploit the rewards given by the environment. This means that these techniques may easily get stuck in local optima. In this work, we propose a novel approach to interpretable reinforcement learning that uses decision trees. We present a two-level optimization scheme that combines the advantages of evolutionary algorithms with the advantages of Q-learning. This way we decompose the problem into two sub-problems: the problem of finding a meaningful and useful decomposition of the state space, and the problem of associating an action to each state. We test the proposed method on three well-known reinforcement learning benchmarks, on which it results competitive with respect to the state-of-the-art in both performance and interpretability. Finally, we perform an ablation study that confirms that using the two-level optimization scheme gives a boost in performance in non-trivial environments with respect to a one-layer optimization technique.

The foundation for the research of summarization in the Czech language was laid by the work of Straka et al. (2018). They published the SumeCzech, a large Czech news-based summarization dataset, and proposed several baseline approaches. However, it is clear from the achieved results that there is a large space for improvement. In our work, we focus on the impact of named entities on the summarization of Czech news articles. First, we annotate SumeCzech with named entities. We propose a new metric ROUGE_NE that measures the overlap of named entities between the true and generated summaries, and we show that it is still challenging for summarization systems to reach a high score in it. We propose an extractive summarization approach Named Entity Density that selects a sentence with the highest ratio between a number of entities and the length of the sentence as the summary of the article. The experiments show that the proposed approach reached results close to the solid baseline in the domain of news articles selecting the first sentence. Moreover, we demonstrate that the selected sentence reflects the style of reports concisely identifying to whom, when, where, and what happened. We propose that such a summary is beneficial in combination with the first sentence of an article in voice applications presenting news articles. We propose two abstractive summarization approaches based on Seq2Seq architecture. The first approach uses the tokens of the article. The second approach has access to the named entity annotations. The experiments show that both approaches exceed state-of-the-art results previously reported by Straka et al. (2018), with the latter achieving slightly better results on SumeCzech's out-of-domain testing set.

This paper presents a novel supervised approach to detecting the chorus segments in popular music. Traditional approaches to this task are mostly unsupervised, with pipelines designed to target some quality that is assumed to define "chorusness," which usually means seeking the loudest or most frequently repeated sections. We propose to use a convolutional neural network with a multi-task learning objective, which simultaneously fits two temporal activation curves: one indicating "chorusness" as a function of time, and the other the location of the boundaries. We also propose a post-processing method that jointly takes into account the chorus and boundary predictions to produce binary output. In experiments using three datasets, we compare our system to a set of public implementations of other segmentation and chorus-detection algorithms, and find our approach performs significantly better.

Seven months after iOS 14 was released, Apple has today revealed that iOS 14.5 will begin rolling out the week of April 26th. At its iPad event, the company declined to offer a specific date, but its press materials said that the new software would arrive "next week." Despite being a .5 release, this is a significant software update that adds a number of features for all iOS-device users. The list of changes includes a prompt to choose how Siri sounds, the ability to unlock FaceID-capable iPhones with an Apple Watch and some big changes to how the software handles your privacy online. We know, from the various beta launches that have happened along the way, that iOS 14.5 will offer users a way of unlocking their phone while wearing a face mask.

In 2018, in late October, a distinctly odd painting appeared at the fine art auction house Christe's. At a distance, the painting looks like a 19th-century portrait of an austere gentleman dressed in black. Contained in a gilt frame, the portly gentleman appears middle-aged; his white-collar insinuates that he is a man of the church. The painting seems unassuming, something expected at an auction house that sells billions of dollars of painting each year. However, upon closer inspection, things get a bit odd.

As more data, better algorithms, and higher computing power continue to shape the future of artificial intelligence (AI), reliable machine learning …