Musical performance requires prediction to operate instruments, to perform in groups and to improvise. We argue, with reference to a number of digital music instruments (DMIs), including two of our own, that predictive machine learning models can help interactive systems to understand their temporal context and ensemble behaviour. We also discuss how recent advances in deep learning highlight the role of prediction in DMIs, by allowing data-driven predictive models with a long memory of past states. We advocate for predictive musical interaction, where a predictive model is embedded in a musical interface, assisting users by predicting unknown states of musical processes. We propose a framework for characterising prediction as relating to the instrumental sound, ongoing musical process, or between members of an ensemble. Our framework shows that different musical interface design configurations lead to different types of prediction. We show that our framework accommodates deep generative models, as well as models for predicting gestural states, or other high-level musical information. We apply our framework to examples from our recent work and the literature, and discuss the benefits and challenges revealed by these systems as well as musical use-cases where prediction is a necessary component.

A semi-recurrent hybrid VAE-GAN model for generating sequential data is introduced. In order to consider the spatial correlation of the data in each frame of the generated sequence, CNNs are utilized in the encoder, generator, and discriminator. The subsequent frames are sampled from the latent distributions obtained by encoding the previous frames. As a result, the dependencies between the frames are maintained. Two testing frameworks for synthesizing a sequence with any number of frames are also proposed. The promising experimental results on piano music generation indicates the potential of the proposed framework in modeling other sequential data such as video.

Some of these models learn a latent space: a lowerdimensional representation that can be mapped to and from the object space. A major advantage of such latent space models is that many operations that would be difficult to perform in the object space, like morphing between two objects in a semantically meaningful way, become straightforward arithmetic in the latent space. It has even been claimed that latent space models can augment human understanding of the object domain [6]. Latent space models have already been trained for several musical concepts including raw waveforms of notes [12], melodies and drum tracks [33], and playlists [38]. Such models are also frequently used for music recommendations [23], where both user "taste" and song "style" are reasoned about in terms of latent vectors. In this paper, we present a latent space model of individual measures of music with multi-instrument polyphony and dynamics. One way to think about such objects is as musical textures; however, we do not model the audio itself but rather use a symbolic representation of the music. This latent space model allows us to perform a number of intuitive operations: - Sample a measure from the prior distribution to generate novel music from scratch.

Over years, a crucial part of data-gathering behavior has revolved around what other people think. With the constantly growing popularity and availability of opinion-driven resources such as personal blogs and online review sites, new challenges and opportunities are emerging as people have started using advanced technologies to make decisions now. Sentiment analysis or opinion mining, refers to the use of computational linguistics, text analytics and natural language processing to identify and extract information from source materials. Sentiment analysis is considered one of the most popular applications of text analytics. The primary aspect of sentiment analysis includes data analysis on the body of the text for understanding the opinion expressed by it and other key factors comprising modality and mood.

Here's where to search for treasure chests in Dusty Divot. Fortnite's Season 4, Week 5 challenges went live this morning. Some of them are pretty easy this week and others are a little bit trickier. One of the riskier challenges this week is searching chests in Dusty Divot, the giant crater created at the outset of Season 4 where Dusty Depot once stood. That's partly because Dusty Divot is a popular, central location that's almost always well-populated.

We all know that Artificial Intelligence (AI) is here to stay. We experience AI everywhere and enjoy its benefits without even realizing it. From streaming video services like Netflix, which learn our viewing behaviors and patterns so we spend our valuable time watching the shows we like best; to digital assistants like Amazon's Echo which can recognize speech patterns to follow our commands and answer our questions; to using AI-powered apps like Lyft or Google Maps to hail the closest ride or navigate around traffic and get from point A to point B, AI is now embedded in our daily lives. Each of these everyday consumer applications uses machine learning (ML) for their AI use cases. But it's not just the consumer technology giants and startups that are using ML technology to power AI-enabled applications; enterprises in virtually every industry are now exploring ML for a wide range of different AI use cases, ranging from fraud detection to medical diagnosis, stock market prediction, and autonomous driving to name just a few.

Libratus is an artificial intelligence developed by Carnegie Mellon University. This AI system has made history by defeating four of the world's best professional poker players in a marathon 20-day poker competition, called "Brains Vs. The developers of Libratus said this victory is statistically significant and not simply a matter of luck. This new milestone in artificial intelligence has implications for any realm in which information is incomplete and opponents sow misinformation Business negotiation, military strategy, cybersecurity and medical treatment planning could all benefit from automated decision-making using a Libratus-like AI. Libratus' victory was made possible by the Pittsburgh Supercomputing Center's Bridges computer, on which the AI computed its strategy before and during the event.

It might not be quite as dramatic as when it was hurtling towards the map in Season 4 of Fortnite: Battle Royale, but the meteorite that crashed into Dusty Divot continues to yield secrets. For weeks now, little robot arms contained within the research facility have been drilling it open: there wasn't much progress at first, but a little while ago we got a glimpse of whatever was inside of it. Now, however, the thing is busted more or less wide open, and we can get a much closer look at the thing in the middle. For one thing, you'll notice that whatever is in there has started glowing red, a sure sign that it is mysterious, awesome, evil, or some combination of all 3. It's a little unclear just what's in there, but to me, it looks like a satellite, something not unlike the orbital probe glider that pairs with the Leviathan skin. My colleague Paul Tassi responded only with "Megatron," saying it looked like an evil robot.

Apple's annual developer conference, WWDC, kicks off on Monday in San Jose, California. We'll get a first look at the newest updates in store for the iPhone, Apple Watch, Mac, and other Apple products. We're not expecting much in terms of whizz-bang feature announcements this year; the company has reportedly decided to take a step back and hone its existing software offerings. However, we'll still see improvements to Apple's operating systems. Apple would be smart to give Siri priority among its consumer-facing updates.

D'Arcy Carden admitted that playing the first incarnation of Janet on the NBC comedy "The Good Place" was not easy. Every line you say and every line your scene partner says, the point is reacting to it, but when your character doesn't react, it's a weird struggle," Carden told Variety of the main challenge she faced when portraying the early version of her character. "I talked about it with [series creator] Mike [Schur] a lot, and he's amazing and so collaborative, and it was very helpful to figure it out together." After figuring out the right balance between human and robot, Carden said that playing Janet started to feel so natural for her. "Now, I feel like I know her so well: She is me, I am her," Carden told Elle last September. In an interview with Collider last January, Carden revealed that the "first season was more challenging" for her and "coming back for the second season felt more like home." "I was like, 'Oh, I know this character!