Artificial intelligence (AI) is expected to transform the productivity and GDP potential of the global economy. According to a report by PWC, strategic investment in different types of AI technology is needed to make that happen. PWC's research shows that 45% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand. This is because AI will drive greater product variety, with increased personalization, attractiveness and affordability over time. AI could contribute up to $15.7 trillion1 to the global economy in 2030, more than the current output of China and India combined.

The world premiere of PlayStation in Concert took place this week, featuring PlayStation game music from the 90s to today, arranged by Jim Fowler and performed by the Royal Philharmonic Orchestra. The Royal Albert Hall was opened by Queen Victoria in 1871, seats more than 5,000, and has hosted events such as the 1968 Eurovision Song Contest, the 100th anniversary of the Royal Variety Performance and the BBC Proms each summer. So what does it mean for video games that they're now being presented in these hallowed halls? And what if a budding composer sees how far video game music has come and wants to get involved? And what is the future of this industry?

Artifical intelligence will bring many benefits to unified communications, but shallow, consumer-grade AI is not currently up to the task of helping enterprises, according to a speaker at the UC EXPO show. Networking analyst Zeus Kerravala told the London-based show earlier this month that a general set of problems persists in UC, mostly relating to system usability. These include uncertainty over who is in a meeting and who should have been invited, not knowing how to join a meeting, share documents or content, or how to make video features work properly. Future AI-enhanced UC systems could improve meetings with features such as: more intuitive call recording or transcription, perhaps based on keywords; facial recognition and automated identification to aid meeting set-up; or features such as intelligent speaker tracking. Looking further ahead, more advanced AI features could offer users recommendations on who should join a team meeting, proactively finding and loading useful content, or generating minutes.

A subsidiary of Google has entered a two-year partnership with Fudan University, the leading university in China's eastern Shanghai municipality, with a focus on emerging technologies such as artificial intelligence. Google China's Education Cooperation Division support Fudan's curriculum related to emerging science and technology, online news outlet The Paper reported, adding that the pair will jointly build a laboratory as well as an exchange center to boost interaction between students in China and the US. Google's China-based education unit has been working with schools in the country since 2006, covering undergraduate, higher vocational education and secondary schools. The projects supported include joint scientific research, curriculum construction, teacher training and information technology education for middle school students. AI is a key focus of Google's development in China.

Earlier this year, Signify announced new integrations for its Philips Hue bulbs that included a Hue Sync app to automatically coordinate your lights with whatever you're doing on your computer. Now, the free Philips Hue Sync app is available for both Mac and PC. The new app allows users to sync Hue smart light levels and colors to video, games and music. The lights can "dance" to the beat of your music track or mimic the hues you see on your screen. ""Following several entertainment pilots, we've refined the way lighting can be used with games, music and video," Jasper Vervoort, the Head of Marketing and Product Management, Home Systems and Luminaires, said in a release.

There's been a lot of talk recently about whether or not AI research is in a bubble. Some people are worried that we're approaching another AI winter -- a period where AI research funding dries up if AI can't deliver on the hype. Sure, there is a lot hype and bad reporting around AI. Just look at all the news reports calling Sophia the "first AI citizen" when it's really just a weird puppet head hooked up to a chat bot: But even if the recent advancements in Deep Learning aren't really intelligence per se and don't get us closer to Strong AI, deep learning has definitely made an impact. It's led to huge advancements in real products the help real people every day, like Google Translate: So let's not argue about whether or not we are in an AI bubble. Instead, let's focus on an even more important issue that we can all get behind -- the absolutely atrocious stock photos used in almost every single news story about AI.

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.