Spotify is the largest on-demand music services in the world and has 299 million monthly users, including 138 million paying subscribers. One of the exciting features of the Spotify is Discover Weekly. It's a custom mixtape of 30 songs they've never listened to before but will probably love, and it's pretty much magic. Songza used to curate playlists manually in the early 2000s which was not able to fulfil the music needs of the consumers as it was developed as on specific interest of the curator. Pandora used tagging the songs manually with attributes like'folk', 'rock' and curating playlist with similar tags using the code. Collaborative filtering: It works by comparing people with similar tastes.

It seems plausible to me that the neural networks in our brain are similar to classification networks, which map inputs into a reduced space learned representation. If so, it may be that psychedelics cause those networks to map to slightly adjacent areas of the learned representation, producing hallucinations that are perceptually adjacent to the inputs. One thing I've seen gaining more traction in recent years, with some small (but quickly growing) evidence behind it and the support of some well known people in the field (David E. Nichols and Dr Robin Carhart-Harris, is that psychedelics change the larger scale network of networks like the default mode network. The idea being that psychedelics can increase or decrease signals through these networks, and that e.g. Going through all paths that aren't normally used for that purpose would explain a lot of the basic effects, and could also explain why you see things in greater detail on psychedelics (I can't find it now but David E. Nichols went through this in a presentation before, I believe he showed that e.g. a lot of visual data is thrown out at the end of the network path, and that psychedelics stop it being discarded and it instead reaches the conscious parts).

Introduction & Scope: Global Data Science and Machine Learning Service Market This in-depth research offering entailing key developments in …

Global Cloud Machine Learning Market Research Report Analysis the marketplace summary, Production Development, Sales, Regional Trade, …

While technology has evolved, allowing newspapers to serve up digital content personalized to the subscriber and offering customer portals to boost subscriptions, the same can't be said for the print side of newspaper businesses. Organizations are still having to manage and allocate resources toward task-heavy print processes instead of focusing on digitally transforming their business models. And so, as the move to digital accelerates, there are some critical steps that can be taken to help close the print and digital divide. According to a survey conducted by Naviga in 2020, approximately 30 percent of all newsroom resources are dedicated to the print newspaper manufacturing process. That's a third of all resources tethered to delivering a physical paper.

Over the past year we've heard AI-generated music in the style of Kanye West, Tupac, and Nas, a lyrically nonsensical (but stylistically pretty believable) track based on the catalogue of Travis Scott, and a fake Nirvana song written by a computer. It's not just musicians that are being tapped for inspiration by artificial intelligence though, as confirmed by a recent bot that's learned to create drawings in the style of Banksy. Named GANksy (after the machine learning framework, a generative adversarial network, on which it's based) the software "was born into the cloud" in September 2020, as detailed in a statement on the creator's website. The statement continues to explain that GANksy was trained using hundreds of images from the portfolio of a "certain street artist". And though Banksy isn't explicitly mentioned by name, it seems pretty clear from the title – plus the fact that the images are eerily reminiscent of his work – that he's the artist in question.

Inspired by the new Netflix original titled'Over the Moon', Microsoft has launched three new modules that guide learners through beginning concepts in data science, machine learning and artificial intelligence (AI). The new Explore Space with "Over the Moon" learning path includes three modules: planning a Moon mission using the Python Pandas Library, predicting meteor showers using Python and VC Code and using AI to recognise objects in images using Azure Custom Vision. "Like the hero Fei Fei, use data to plan your own mission to the moon. Ensure your rocket can not only get you there, but also bring you and all your moon rocks safely back to Earth. Analyse and visualize datasets with common data cleansing practices," Microsoft said in a statement on Friday.

In this paper, we propose a new approach to cover song identification using a CNN (convolutional neural network). Most previous studies extract the feature vectors that characterize the cover song relation from a pair of songs and used it to compute the (dis)similarity between the two songs. Based on the observation that there is a meaningful pattern between cover songs and that this can be learned, we have reformulated the cover song identification problem in a machine learning framework. To do this, we first build the CNN using as an input a cross-similarity matrix generated from a pair of songs. We then construct the data set composed of cover song pairs and non-cover song pairs, which are used as positive and negative training samples, respectively. The trained CNN outputs the probability of being in the cover song relation given a cross-similarity matrix generated from any two pieces of music and identifies the cover song by ranking on the probability. Experimental results show that the proposed algorithm achieves performance better than or comparable to the state-of-the-art.

The linear submodular bandit problem was proposed to simultaneously address diversified retrieval and online learning in a recommender system. If there is no uncertainty, this problem is equivalent to a submodular maximization problem under a cardinality constraint. However, in some situations, recommendation lists should satisfy additional constraints such as budget constraints, other than a cardinality constraint. Thus, motivated by diversified retrieval considering budget constraints, we introduce a submodular bandit problem under the intersection of $l$ knapsacks and a $k$-system constraint. Here $k$-system constraints form a very general class of constraints including cardinality constraints and the intersection of $k$ matroid constraints. To solve this problem, we propose a non-greedy algorithm that adaptively focuses on a standard or modified upper-confidence bound. We provide a high-probability upper bound of an approximation regret, where the approximation ratio matches that of a fast offline algorithm. Moreover, we perform experiments under various combinations of constraints using a synthetic and two real-world datasets and demonstrate that our proposed methods outperform the existing baselines.

Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction. For example, a female character in a story is often portrayed as passive and powerless ("She daydreams about being a doctor") while a man is portrayed as more proactive and powerful ("He pursues his dream of being a doctor"). We formulate *Controllable Debiasing*, a new revision task that aims to rewrite a given text to correct the implicit and potentially undesirable bias in character portrayals. We then introduce PowerTransformer as an approach that debiases text through the lens of connotation frames (Sap et al., 2017), which encode pragmatic knowledge of implied power dynamics with respect to verb predicates. One key challenge of our task is the lack of parallel corpora. To address this challenge, we adopt an unsupervised approach using auxiliary supervision with related tasks such as paraphrasing and self-supervision based on a reconstruction loss, building on pretrained language models. Through comprehensive experiments based on automatic and human evaluations, we demonstrate that our approach outperforms ablations and existing methods from related tasks. Furthermore, we demonstrate the use of PowerTransformer as a step toward mitigating the well-documented gender bias in character portrayal in movie scripts.